A Review of Machine Learning Algorithms for Text-Documents Classification
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paper_title: Machine learning in automated text categorization

paper_content:

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
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paper_title: Stock trend prediction using news articles : a text mining approach

paper_content:

Stock market prediction with data mining techniques is one of the most important issues to be investigated. Mining textual documents and time series concurrently, such as predicting the movements ...
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paper_title: Feature selection methods for text classification

paper_content:

We consider feature selection for text classification both the- oretically and empirically. Our main result is an unsuper- vised feature selection strategy for which we give worst-case theoretical guarantees on the generalization power of the resultant classification function ˜ f with respect to the classi- fication function f obtained when keeping all the features. To the best of our knowledge, this is the first feature selec- tion method with such guarantees. In addition, the anal- ysis leads to insights as to when and why this feature se- lection strategy will perform well in practice. We then use the TechTC-100, 20-Newsgroups, and Reuters-RCV2 data sets to evaluate empirically the performance of this and two simpler but related feature selection strategies against two commonly-used strategies. Our empirical evaluation shows that the strategy with provable performance guarantees per- forms well in comparison with other commonly-used feature selection strategies. In addition, it performs better on cer- tain datasets under very aggressive feature selection.
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paper_title: A Brief Survey of Text Mining

paper_content:

The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this article, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining.
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paper_title: OCFS: optimal orthogonal centroid feature selection for text categorization

paper_content:

Text categorization is an important research area in many Information Retrieval (IR) applications. To save the storage space and computation time in text categorization, efficient and effective algorithms for reducing the data before analysis are highly desired. Traditional techniques for this purpose can generally be classified into feature extraction and feature selection. Because of efficiency, the latter is more suitable for text data such as web documents. However, many popular feature selection techniques such as Information Gain (IG) andχ2-test (CHI) are all greedy in nature and thus may not be optimal according to some criterion. Moreover, the performance of these greedy methods may be deteriorated when the reserved data dimension is extremely low. In this paper, we propose an efficient optimal feature selection algorithm by optimizing the objective function of Orthogonal Centroid (OC) subspace learning algorithm in a discrete solution space, called Orthogonal Centroid Feature Selection (OCFS). Experiments on 20 Newsgroups (20NG), Reuters Corpus Volume 1 (RCV1) and Open Directory Project (ODP) data show that OCFS is consistently better than IG and CHI with smaller computation time especially when the reduced dimension is extremely small.
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paper_title: Measures of Rule Quality for Feature Selection in Text Categorization

paper_content:

Text Categorization is the process of assigning documents to a set of previously fixed categories. A lot of research is going on with the goal of automating this time-consuming task. Several different algorithms have been applied, and Support Vector Machines have shown very good results. In this paper we propose a new family of measures taken from the Machine Learning environment to apply them to feature reduction task. The experiments are performed on two different corpus (Reuters and Ohsumed). The results show that the new family of measures performs better than the traditional Information Theory measures.
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paper_title: Rich document representation and classification: An analysis

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There are three factors involved in text classification. These are classification model, similarity measure and document representation model. In this paper, we will focus on document representation and demonstrate that the choice of document representation has a profound impact on the quality of the classifier. In our experiments, we have used the centroid-based text classifier, which is a simple and robust text classification scheme. We will compare four different types of document representations: N-grams, Single terms, phrases and RDR which is a logic-based document representation. The N-gram representation is a string-based representation with no linguistic processing. The Single term approach is based on words with minimum linguistic processing. The phrase approach is based on linguistically formed phrases and single words. The RDR is based on linguistic processing and representing documents as a set of logical predicates. We have experimented with many text collections and we have obtained similar results. Here, we base our arguments on experiments conducted on Reuters-21578. We show that RDR, the more complex representation, produces more effective classifier on Reuters-21578, followed by the phrase approach.
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paper_title: Beyond TFIDF Weighting for Text Categorization in the Vector Space Model

paper_content:

KNN and SVM are two machine learning approaches to Text Categorization (TC) based on the Vector Space Model. In this model, borrowed from Information Retrieval, documents are represented as a vector where each component is associated with a particular word from the vocabulary. Traditionally, each component value is assigned using the information retrieval TFIDF measure. While this weighting method seems very appropriate for IR, it is not clear that it is the best choice for TC problems. Actually, this weighting method does not leverage the information implicitly contained in the categorization task to represent documents. In this paper, we introduce a new weighting method based on statistical estimation of the importance of a word for a specific categorization problem. This method also has the benefit to make feature selection implicit, since useless features for the categorization problem considered get a very small weight. Extensive experiments reported in the paper shows that this new weighting method improves significantly the classification accuracy as measured on many categorization tasks.
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paper_title: Class dependent feature scaling method using naive Bayes classifier for text datamining

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The problem of feature selection is to find a subset of features for optimal classification. A critical part of feature selection is to rank features according to their importance for classification. The naive Bayes classifier has been extensively used in text categorization. We have developed a new feature scaling method, called class-dependent-feature-weighting (CDFW) using naive Bayes (NB) classifier. A new feature scaling method, CDFW-NB-RFE, combines CDFW and recursive feature elimination (RFE). Our experimental results showed that CDFW-NB-RFE outperformed other popular feature ranking schemes used on text datasets.
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paper_title: An Optimal SVM-Based Text Classification Algorithm

paper_content:

The goal of a text classification system is to determine whether a given document belongs to which of the predefined categories. An optimal SVM algorithm for text classification via multiple optimal strategies is proposed in this paper. The experimental results indicate that the proposed optimal classification algorithm yields much better performance than other conventional algorithms.
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paper_title: Measures of Rule Quality for Feature Selection in Text Categorization

paper_content:

Text Categorization is the process of assigning documents to a set of previously fixed categories. A lot of research is going on with the goal of automating this time-consuming task. Several different algorithms have been applied, and Support Vector Machines have shown very good results. In this paper we propose a new family of measures taken from the Machine Learning environment to apply them to feature reduction task. The experiments are performed on two different corpus (Reuters and Ohsumed). The results show that the new family of measures performs better than the traditional Information Theory measures.
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paper_title: Text feature selection using ant colony optimization

paper_content:

Feature selection and feature extraction are the most important steps in classification systems. Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features which would be impossible to process further. One of the problems in which feature selection is essential is text categorization. A major problem of text categorization is the high dimensionality of the feature space; therefore, feature selection is the most important step in text categorization. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present a novel feature selection algorithm that is based on ant colony optimization. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of genetic algorithm, information gain and CHI on the task of feature selection in Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm.
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paper_title: Extremely fast text feature extraction for classification and indexing

paper_content:

Most research in speeding up text mining involves algorithmic improvements to induction algorithms, and yet for many large scale applications, such as classifying or indexing large document repositories, the time spent extracting word features from texts can itself greatly exceed the initial training time. This paper describes a fast method for text feature extraction that folds together Unicode conversion, forced lowercasing, word boundary detection, and string hash computation. We show empirically that our integer hash features result in classifiers with equivalent statistical performance to those built using string word features, but require far less computation and less memory.
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paper_title: Feature selection with a measure of deviations from Poisson in text categorization

paper_content:

To improve the performance of automatic text classification, it is desirable to reduce a high dimensionality of the feature space. In this paper, we propose a new measure for selecting features, which estimates term importance based on how largely the probability distribution of each term deviates from the standard Poisson distribution. In information retrieval literatures, the deviation from Poisson has been used as a measure for weighting keywords and this motivates us to adopt the deviation from Poisson as a measure for feature selection in text classification tasks. The proposed measure is constructed so as to have the same computational complexity with other standard measures used for feature selection. To test the effectiveness of our method, we conducted evaluation experiments on Reuters-21578 corpus with support vector machine and k-NN classifiers. In the experiments, we performed binary classifications to determine whether each of the test documents belongs to a certain target category or not. For the target category, each of the top 10 categories of Reuters-21578 was used because of enough numbers of training and test documents. Four measures were used for feature selection; information gain (IG), @g^2-statistic, Gini index and the proposed measure in this work. Both the proposed measure and Gini index proved to be better than IG and @g^2-statistic in terms of macro-averaged and micro-averaged values of F"1, especially at higher vocabulary reduction levels.
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paper_title: Ambiguity in text mining

paper_content:

Text Mining tasks include text categorization, text clustering, concept/entity extraction, document summarization, and entity relation modeling. In this paper, the focus is given to concept/entity extraction only. The major challenging issue in extracting concept/entity from texts is natural language words are always ambiguous. Up to now, not much research in text mining especially in concept/entity extraction has focused on the ambiguity problem. This paper addresses ambiguity issues in natural language texts, and presents a new technique for resolving ambiguity problem in extracting concept/entity from texts. The technique is developed by applying possibility theory, fuzzy set, and knowledge about the context to lexical semantics.
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paper_title: Machine learning in automated text categorization

paper_content:

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
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paper_title: Automatic classification of Web pages based on the concept of domain ontology

paper_content:

The use of ontology in order to provide a mechanism to enable machine reasoning has continuously increased during the last few years. This paper suggests an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontology-based document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two predefined categories per given document features. In this paper, Web pages are classified in real time not with experimental data or a learning process, but by similar calculations between the terminology information extracted from Web pages and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined.
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paper_title: Overview and semantic issues of text mining

paper_content:

Text mining refers to the discovery of previously unknown knowledge that can be found in text collections. In recent years, the text mining field has received great attention due to the abundance of textual data. A researcher in this area is requested to cope with issues originating from the natural language particularities. This survey discusses such semantic issues along with the approaches and methodologies proposed in the existing literature. It covers syntactic matters, tokenization concerns and it focuses on the different text representation techniques, categorisation tasks and similarity measures suggested.
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paper_title: Text mining workflows construction with support of ontologies

paper_content:

In order to fulfill the ambitious goals of semantic Web services frameworks, there is important to support not only the Functional Semantics, but also the data semantics that is especially important by the invocation activity. This is together with bringing QoS Semantics to the Web Services also our goal in the context of the research project "semantic composition of web and grid services (APW- 0391-06)". In the paper we present a semantic model intended to describe grid data and text mining services, whereby the semantic information about the functional, data and QoS models is utilized to construct the resulting executable workflow.
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paper_title: Ontology-Based Automatic Classification and Ranking for Web Documents

paper_content:

The process of Web document classification involves calculating similarities between documents and categories by using the information extracted from them. In recent years, ontology-based Web documents classification method is introduced to solve the problem of classifier training and not considering semantic relations between words in traditional machine learning algorithms. However, previous works on ontology-based web documents classification miss some important issues of automatic ontology construction and ranking of classified documents. In order to solve these problems, this paper proposes an ontology-based web documents classification and ranking method. Firstly, weighted terms set are extracted from web documents, and ontology is build up by using an effective ontology construction method which clarifies and augments an existent ontology; then similarity score between documents and ontology is computed based on WordNet by using Earth Mover's Distance (EMD) method; finally, web documents are assigned to categories according to the similarity score, and a simple ranking method is used to sort the documents in the same categories. The experiment result shows our classification algorithm achieves better precision and recall compare with adaptive KNN method, and is competitive with SVM method, the ranking method also has good performance.
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paper_title: An Automatic Document Classifier System based on Naíve Bayes Classifier and Ontology

paper_content:

An automatic document classifier system based on ontology and the naive Bayes classifier is proposed in this paper. The main concept is to first establish a keyword synonymous table by experts for narrowing down the range and getting the consistency of keywords. The formal concept analysis is then used for establishing knowledge ontology through the complex categories and attributes relation. Finally, the ontology is applied to a naive Bayes classifier to get the automatic document classifier system. In this system, 319 documents divided into 11 categories are used to assess the effectiveness of classification, where 224 and 95 documents are the training and testing documents respectively, and the F1-measure is as the assessment criteria. The experimental results show that nine from 11 categories reaches 80% effectiveness of the documents classification, whereas the other two categories reached over 60% effectiveness of the documents classification. In sum, the average effectiveness of document classification in 11 categories is about 89%. Thus, the automatic classifier system can indeed reach the effectiveness of document classification.
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paper_title: Ontology-based Semantic Classification of Unstructured Documents

paper_content:

As more and more knowledge and information becomes available through computers, a critical capability of systems supporting knowledge management is the classification of documents into categories that are meaningful to the user. In a step beyond the use of keywords, we developed a system that analyzes the sentences contained in unstructured or semi-structured documents, and utilizes an ontology reflecting the domain knowledge for a semantic classification of the documents. An experimental system has been implemented for the analysis of small documents in combination with a limited ontology; an extension to larger sets of documents and extended ontologies, together with an application to practical tasks, is the focus of ongoing work.
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paper_title: Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup

paper_content:

Motivation: The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases. Text mining may provide useful tools to assist in the curation process. To date, the lack of standards has made it impossible to determine whether text mining techniques are sufficiently mature to be useful. Results: We report on a Challenge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup. We provided a training corpus of 862 articles consisting of journal articles curated in FlyBase, along with the associated lists of genes and gene products, as well as the relevant data fields from FlyBase. For the test, we provided a corpus of 213 new (‘blind’) articles; the 18 participating groups provided systems that flagged articles for curation, based on whether the article contained experimental evidence for gene expression products. We report on the evaluation results and describe the techniques used by the top performing groups.
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paper_title: The conceptual basis for mediation services

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As information systems grow, they depend increasingly on diverse, heterogeneous resources, such as databases, knowledge bases, bibliographic files, Web-based information, computational facilities, digital libraries, geographic information systems, and simulations. Users typically develop and maintain these resources autonomously. Mediator modules comprise a layer of intelligent middleware services in information systems that link data resources and application programs. Currently, system developers must build intelligent mediators by carefully acquiring domain knowledge and handcrafting the required code. This article presents the conceptual underpinning for automating mediation.
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paper_title: Relevance Feedback in Information Retrieval

paper_content:

1332840 Primer compositions DOW CORNINGCORP 6 Oct 1971 [30 Dec 1970] 46462/71 Heading C3T [Also in Divisions B2 and C4] A primer composition comprises 1 pbw of tetra ethoxy or propoxy silane or poly ethyl or propyl silicate or any mixture thereof, 0A75-2A5 pbw of bis(acetylacetonyl) diisopropyl titanate, 0A75- 5 pbw of a compound CF 3 CH 2 CH 2 Si[OSi(CH 3 ) 2 - X] 3 wherein each X is H or -CH 2 CH 2 Si- (OOCCH 3 ) 3 , at least one being the latter, and 1-20 pbw of a ketone, hydrocarbon or halohydrocarbon solvent boiling not above 150‹ C. In the examples 1 pbw each of bis(acetylacetonyl)diisopropyl titanate, polyethyl silicate and are dissolved in 10 pbw of acetone or in 9 pbw of light naphtha and 1 of methylisobutylketone. The solutions are used to prime Ti panels, to which a Pt-catalysed room-temperature vulcanizable poly-trifluoropropylmethyl siloxanebased rubber is then applied.
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paper_title: Rough set based hybrid algorithm for text classification

paper_content:

Automatic classification of text documents, one of essential techniques for Web mining, has always been a hot topic due to the explosive growth of digital documents available on-line. In text classification community, k-nearest neighbor (kNN) is a simple and yet effective classifier. However, as being a lazy learning method without premodelling, kNN has a high cost to classify new documents when training set is large. Rocchio algorithm is another well-known and widely used technique for text classification. One drawback of the Rocchio classifier is that it restricts the hypothesis space to the set of linear separable hyperplane regions. When the data does not fit its underlying assumption well, Rocchio classifier suffers. In this paper, a hybrid algorithm based on variable precision rough set is proposed to combine the strength of both kNN and Rocchio techniques and overcome their weaknesses. An experimental evaluation of different methods is carried out on two common text corpora, i.e., the Reuters-21578 collection and the 20-newsgroup collection. The experimental results indicate that the novel algorithm achieves significant performance improvement.
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paper_title: Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

paper_content:

Text categorization presents unique challenges due to the large number of attributes present in the data set, large number of training samples, attribute dependency, and multi-modality of categories. Existing classification techniques have limited applicability in the data sets of these natures. In this paper, we present a Weight Adjusted k-Nearest Neighbor (WAKNN) classification that learns feature weights based on a greedy hill climbing technique. We also present two performance optimizations of WAKNN that improve the computational performance by a few orders of magnitude, but do not compromise on the classification quality. We experimentally evaluated WAKNN on 52 document data sets from a variety of domains and compared its performance against several classification algorithms, such as C4.5, RIPPER, Naive-Bayesian, PEBLS and VSM. Experimental results on these data sets confirm that WAKNN consistently outperforms other existing classification algorithms.
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paper_title: Using phrases as features in email classification

paper_content:

In this paper, we report our experience on the use of phrases as basic features in the email classification problem. We performed extensive empirical evaluation using our large email collections and tested with three text classification algorithms, namely, a naive Bayes classifier and two k-NN classifiers using TF-IDF weighting and resemblance respectively. The investigation includes studies on the effect of phrase size, the size of local and global sampling, the neighbourhood size, and various methods to improve the classification accuracy. We determined suitable settings for various parameters of the classifiers and performed a comparison among the classifiers with their best settings. Our result shows that no classifier dominates the others in terms of classification accuracy. Also, we made a number of observations on the special characteristics of emails. In particular, we observed that public emails are easier to classify than private ones.
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paper_title: Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts

paper_content:

Customization and personalization services are a critical success factor for Internet stores and Web service providers. This paper studies personalized recommendation techniques that suggest products or services to the customers of Internet storefronts based on their demographics or past purchasing behavior. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and rule-based matching. In the rule-based approach to personalized recommendation, marketing rules for personalization are usually obtained from marketing experts and used to perform inferencing based on customer data. However, it is difficult to extract marketing rules from marketing experts, and to validate and maintain the constructed knowledge base. This paper proposes a marketing rule-extraction technique for personalized recommendation on Internet storefronts using machine learning techniques, and especially decisiontree induction techniques. Using tree induction techniques, data-mining tools can generate marketing rules that match customer demographics to product categories. The extracted rules provide personalized advertisement selection when a customer visits an Internet store. An experiment is performed to evaluate the effectiveness of the proposed approach with preference scoring and random selection.
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paper_title: Text categorization with support vector machines: Learning with many relevant features

paper_content:

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.
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paper_title: Automated learning of decision rules for text categorization

paper_content:

We describe the results of extensive experiments using optimized rule-based induction methods on large document collections. The goal of these methods is to discover automatically classification patterns that can be used for general document categorization or personalized filtering of free text. Previous reports indicate that human-engineered rule-based systems, requiring many man-years of developmental efforts, have been successfully built to “read” documents and assign topics to them. We show that machine-generated decision rules appear comparable to human performance, while using the identical rule-based representation. In comparison with other machine-learning techniques, results on a key benchmark from the Reuters collection show a large gain in performance, from a previously reported 67% recall/precision breakeven point to 80.5%. In the context of a very high-dimensional feature space, several methodological alternatives are examined, including universal versus local dictionaries, and binary versus frequency-related features.
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paper_title: Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks

paper_content:

Earlier methods on spam filtering usually compare the contents of emails against specific keywords, which are not robust as the spammers frequently change the terms used in emails. This paper presents a hybrid method of rule-based processing and back-propagation neural networks for spam filtering. Instead of using keywords, this study utilize the spamming behaviors as features for describing emails. A rule-based process is first employed to identify and digitize the spamming behaviors observed from the headers and syslogs of emails. An enhanced BPNN with a weighted learning strategy is designed as the classification mechanism. Since spamming behaviors are infrequently changed, compared with that of keywords used in spams, the proposed method is more robust with respect to the change of time. The experimental results show that the proposed method is useful in identifying spam emails.
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paper_title: Towards language independent automated learning of text categorization models

paper_content:

We describe the results of extensive machine learning experiments on large collections of Reuters’ English and German newswires. The goal of these experiments was to automatically discover classification patterns that can be used for assignment of topics to the individual newswires. Our results with the English newswire collection show a very large gain in performance as compared to published benchmarks, while our initial results with the German newswires appear very promising. We present our methodology, which seems to be insensitive to the language of the document collections, and discuss issues related to the differences in results that we have obtained for the two collections.
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paper_title: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

paper_content:

The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier‘s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article‘s results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.
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paper_title: Some Effective Techniques for Naive Bayes Text Classification

paper_content:

While naive Bayes is quite effective in various data mining tasks, it shows a disappointing result in the automatic text classification problem. Based on the observation of naive Bayes for the natural language text, we found a serious problem in the parameter estimation process, which causes poor results in text classification domain. In this paper, we propose two empirical heuristics: per-document text normalization and feature weighting method. While these are somewhat ad hoc methods, our proposed naive Bayes text classifier performs very well in the standard benchmark collections, competing with state-of-the-art text classifiers based on a highly complex learning method such as SVM
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paper_title: Document Classification Methods for Organizing Explicit Knowledge

paper_content:

A lifting apparatus for an agricultural implement, the apparatus includes first and second frame members, with the second frame member pivotally movable relative to the first member about a horizontal axis. To move the second member relative to the first member there is an actuator pivotally attached to the first member and a link pivotally attached to the second member and actuator. The second member has an abutment to engage the link to cause it to pivot with the second through a limited angle, after which the link disengages with the abutment to cause further pivoting of the second member.
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paper_title: Automatic Text Categorization Using Neural Networks

paper_content:

This paper presents the results obtained from a series of experiments in automatic text categorization of MEDLINE articles. The main goal ofthis research is to build a counter propagation network and to train it in assigning MeSH phrases based on term frequency of single words from title and abstract. The experiments compare the performance of the counterpropagation network against a backpropagation neural network trained for the same purpose. Results obtained by using a set of 2,344 MEDLINE documents are presented and discussed.
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paper_title: Stock trend prediction using news articles : a text mining approach

paper_content:

Stock market prediction with data mining techniques is one of the most important issues to be investigated. Mining textual documents and time series concurrently, such as predicting the movements ...
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paper_title: Development of a patent document classification and search platform using a back-propagation network

paper_content:

Abstract In order to process large numbers of explicit knowledge documents such as patents in an organized manner, automatic document categorization and search are required. In this paper, we develop a document classification and search methodology based on neural network technology that helps companies manage patent documents more effectively. The classification process begins by extracting key phrases from the document set by means of automatic text processing and determining the significance of key phrases according to their frequency in text. In order to maintain a manageable number of independent key phrases, correlation analysis is applied to compute the similarities between key phrases. Phrases with higher correlations are synthesized into a smaller set of phrases. Finally, the back-propagation network model is adopted as a classifier. The target output identifies a patent document’s category based on a hierarchical classification scheme, in this case, the international patent classification (IPC) standard. The methodology is tested using patents related to the design of power hand-tools. Related patents are automatically classified using pre-trained neural network models. In the prototype system, two modules are used for patent document management. The automatic classification module helps the user classify patent documents and the search module helps users find relevant and related patent documents. The result shows an improvement in document classification and identification over previously published methods of patent document management.
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paper_title: Bayesian case-based reasoning with neural networks

paper_content:

Given a problem, a case-based reasoning (CBR) system will search its case memory and use the stored cases to find the solution, possibly modifying retrieved cases to adapt to the required input specifications. A neural network architecture is introduced for efficient CBR. It is shown how a rigorous Bayesian probability propagation algorithm can be implemented as a feedforward neural network and adapted for CBR. In the authors' approach, the efficient indexing problem of CBR is naturally implemented by the parallel architecture, and heuristic matching is replaced by a probability metric. This allows their CBR to perform theoretically sound Bayesian reasoning. It is shown how the probability propagation actually offers a solution to the adaptation problem in a very natural way. >
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paper_title: Research of Text Classification Technology based on Genetic Annealing Algorithm

paper_content:

Text classification has received extensive attention in recent years, which is an important means of data mining. This paper analyzed basic theory and general structure of text classification, given a text classification method based on improved genetic algorithms, introduced simulated annealing mechanism of genetic algorithm to solve the precocious easy, local optimum, and so on, using the Roocchio feedback model to achieve feedback and self-learning of text classification. Experiment, we used this method to achieve a text classification system and the traditional KNN classification method were compared, results showed that the training methods for text classification has good accuracy and recall rate.
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paper_title: Document Classification Methods for Organizing Explicit Knowledge

paper_content:

A lifting apparatus for an agricultural implement, the apparatus includes first and second frame members, with the second frame member pivotally movable relative to the first member about a horizontal axis. To move the second member relative to the first member there is an actuator pivotally attached to the first member and a link pivotally attached to the second member and actuator. The second member has an abutment to engage the link to cause it to pivot with the second through a limited angle, after which the link disengages with the abutment to cause further pivoting of the second member.
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paper_title: An Optimal SVM-Based Text Classification Algorithm

paper_content:

The goal of a text classification system is to determine whether a given document belongs to which of the predefined categories. An optimal SVM algorithm for text classification via multiple optimal strategies is proposed in this paper. The experimental results indicate that the proposed optimal classification algorithm yields much better performance than other conventional algorithms.
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paper_title: Construction of supervised and unsupervised learning systems for multilingual text categorization

paper_content:

Due to the availability of a huge amount of textual data from a variety of sources, users of internationally distributed information regions need effective methods and tools that enable them to discover, retrieve and categorize relevant information, in whatever language and form it may have been stored. This drives a convergence of numerous interests from diverse research communities focusing on the issues related to multilingual text categorization. In this work, we implemented and measured the performance of the leading supervised and unsupervised approaches for multilingual text categorization. We selected support vector machines (SVM) as representative of supervised techniques as well as latent semantic indexing (LSI) and self-organizing maps (SOM) techniques as our selective ones of unsupervised methods for system implementation. The preliminary results show that our platform models including both supervised and unsupervised learning methods have the potentials for multilingual text categorization.
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paper_title: Measures of Rule Quality for Feature Selection in Text Categorization

paper_content:

Text Categorization is the process of assigning documents to a set of previously fixed categories. A lot of research is going on with the goal of automating this time-consuming task. Several different algorithms have been applied, and Support Vector Machines have shown very good results. In this paper we propose a new family of measures taken from the Machine Learning environment to apply them to feature reduction task. The experiments are performed on two different corpus (Reuters and Ohsumed). The results show that the new family of measures performs better than the traditional Information Theory measures.
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paper_title: The Nature of Statistical Learning Theory

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Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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paper_title: An efficient document classification model using an improved back propagation neural network and singular value decomposition

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This paper proposed a new improved method for back propagation neural network, and used an efficient method to reduce the dimension and improve the performance. The traditional back propagation neural network (BPNN) has the drawbacks of slow learning and is easy to trap into a local minimum, and it will lead to a poor performance and efficiency. In this paper, we propose the learning phase evaluation back propagation neural network (LPEBP) to improve the traditional BPNN. We adopt a singular value decomposition (SVD) technique to reduce the dimension and construct the latent semantics between terms. Experimental results show that the LPEBP is much faster than the traditional BPNN. It also enhances the performance of the traditional BPNN. The SVD technique cannot only greatly reduce the high dimensionality but also enhance the performance. So SVD is to further improve the document classification systems precisely and efficiently.
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paper_title: Text Classification Using Machine Learning Techniques

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Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. In general, text classification plays an important role in information extraction and summarization, text retrieval, and question- answering. This paper illustrates the text classification process using machine learning techniques. The references cited cover the major theoretical issues and guide the researcher to interesting research directions.
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paper_title: Text line and word segmentation of handwritten documents

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In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.
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paper_title: Associative Naïve Bayes classifier: Automated linking of gene ontology to medline documents

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We demonstrate a text-mining method, called associative Naive Bayes (ANB) classifier, for automated linking of MEDLINE documents to gene ontology (GO). The approach of this paper is a nontrivial extension of document classification methodology from a fixed set of classes C={c"1,c"2,...,c"n} to a knowledge hierarchy like GO. Due to the complexity of GO, we use a knowledge representation structure. With that structure, we develop the text mining classifier, called ANB classifier, which automatically links Medline documents to GO. To check the performance, we compare our datasets under several well-known classifiers: NB classifier, large Bayes classifier, support vector machine and ANB classifier. Our results, described in the following, indicate its practical usefulness.
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paper_title: Semi-Supervised Document Retrieval

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This paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRank, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed recently. The advantages include the use of limited amount of labeled data and rich model representation. To do so, the method adopts a semi-supervised learning framework in ranking model construction. Specifically, given a small number of labeled documents with respect to some queries, the method effectively labels the unlabeled documents for the queries. It then uses all the labeled data to train a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning theory is given for the data labeling process. Experimental results on three benchmark datasets and one web search dataset indicate that SSRank consistently and almost always significantly outperforms the baseline methods (unsupervised and supervised learning methods), given the same amount of labeled data. This is because SSRank can effectively leverage the use of unlabeled data in learning.
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paper_title: Rough set based hybrid algorithm for text classification

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Automatic classification of text documents, one of essential techniques for Web mining, has always been a hot topic due to the explosive growth of digital documents available on-line. In text classification community, k-nearest neighbor (kNN) is a simple and yet effective classifier. However, as being a lazy learning method without premodelling, kNN has a high cost to classify new documents when training set is large. Rocchio algorithm is another well-known and widely used technique for text classification. One drawback of the Rocchio classifier is that it restricts the hypothesis space to the set of linear separable hyperplane regions. When the data does not fit its underlying assumption well, Rocchio classifier suffers. In this paper, a hybrid algorithm based on variable precision rough set is proposed to combine the strength of both kNN and Rocchio techniques and overcome their weaknesses. An experimental evaluation of different methods is carried out on two common text corpora, i.e., the Reuters-21578 collection and the 20-newsgroup collection. The experimental results indicate that the novel algorithm achieves significant performance improvement.
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paper_title: Discretizing Continuous Attributes in AdaBoost for Text Categorization

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We focus on two recently proposed algorithms in the family of "boosting"-based learners for automated text classification, ADABOOST. MH and ADABOOST.MHKR. While the former is a realization of the well-known ADABOOST algorithm specifically aimed at multilabel text categorization, the latter is a generalization of the former based on the idea of learning a committee of classifier sub-committees. Both algorithms have been among the best performers in text categorization experiments so far. ::: ::: A problem in the use of both algorithms is that they require documents to be represented by binary vectors, indicating presence or absence of the terms in the document. As a consequence, these algorithms cannot take full advantage of the "weighted" representations (consisting of vectors of continuous attributes) that are customary in information retrieval tasks, and that provide a much more significant rendition of the document's content than binary representations. ::: ::: In this paper we address the problem of exploiting the potential of weighted representations in the context of ADABOOST-like algorithms by discretizing the continuous attributes through the application of entropy-based discretization methods. We present experimental results on the Reuters-21578 text categorization collection, showing that for both algorithms the version with discretized continuous attributes outperforms the version with traditional binary representations.
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paper_title: Discovering implicit intention-level knowledge from natural-language texts

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In this paper, we propose a new approach to automatic discovery of implicit rhetorical information from texts based on evolutionary computation methods. In order to guide the search for rhetorical connections from natural-language texts, the model uses previously obtained training information which involves semantic and structural criteria. The main features of the model and new designed operators and evaluation functions are discussed, and the different experiments assessing the robustness and accuracy of the approach are described. Experimental results show the promise of evolutionary methods for rhetorical role discovery.
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paper_title: Combining Multiple Classifiers Using Dempster’s Rule of Combination for Text Categorization

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In this paper, we present an investigation into the combination of four different classification methods for text categorization using Dempster’s rule of combination. These methods include the Support Vector Machine, kNN (nearest neighbours), kNN model-based approach (kNNM), and Rocchio methods. We first present an approach for effectively combining the different classification methods. We then apply these methods to a benchmark data collection of 20-newsgroup, individually and in combination. Our experimental results show that the performance of the best combination of the different classifiers on the 10 groups of the benchmark data can achieve 91.07% classification accuracy, which is 2.68% better than that of the best individual method, SVM, on average.
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paper_title: A hybrid learning algorithm for text classification

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ABSTRACT Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification that requires fewer documents for training. Instead of using words, word relation i.e association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. Experimental results show that the classifier build this way is more accurate than the existing text classification systems. 1. INTRODUCTION Text classification has become one of the most important techniques in the text data mining. The task is to automatically classify documents into predefined classes based on their content. Many algorithms have been developed to deal with automatic text classification [3]. With the existing algorithms, a number of newly established processes are involving in the automation of text classification. It has been observed that for the purpose of text classification the concept of association rule is very well known. Association rule mining [1] finds interesting association or correlation relationships among a large set of data items [4]. The discovery of these relationships among huge amounts of transaction records can help in many decision making process. On the other hand, the Naive Bayes classifier uses the maximum a posteriori estimation for learning a classifier. It assumes that the occurrence of each word in a document is conditionally independent of all other words in that document given its class [3]. Although the Naive Bayes works well in many studies [7], it requires a large number of training documents for learning accurately. Genetic algorithm starts with an initial population which is created consisting of randomly generated rules. Each rule can be represented by a string of bits. Based on the notion of survival of the fittest, a new population is formed to consist of the fittest rules in the current population, as well as offspring of these rules. Typically, the fitness of a rule is assessed by its classification accuracy on a set of training examples. This paper presents a new algorithm for text classification. Instead of using words, word relation i.e association rules is used to derive feature set from pre-classified text documents. The concept of Naive Bayes Classifier is then used on derived features and finally a concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.
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paper_title: A simple, structure-sensitive approach for web document classification

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In this paper we describe a new approach to classification of web documents. Most web classification methods are based on the vector space document representation of information retrieval. Recently the graph based web document representation model was shown to outperform the traditional vector representation using k-Nearest Neighbor (k-NN) classification algorithm. Here we suggest a new hybrid approach to web document classification built upon both, graph and vector representations. K-NN algorithm and three benchmark document collections were used to compare this method to graph and vector based methods separately. Results demonstrate that we succeed in most cases to outperform graph and vector approaches in terms of classification accuracy along with a significant reduction in classification time.
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paper_title: An examination of feature selection frameworks in text categorization

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Feature selection, an important task in text categorization, is used for the purpose of dimensionality reduction. Feature selection basically can be performed locally and globally. For local selection, distinct feature sets are derived from different classes. The number of feature set is thus depended on the number of class. In contrary, only one universal feature set will be used in global feature selection. It is assumed that the feature set should preserve the characteristic of all classes. Furthermore, feature selection can also be carried out based on relevant feature set only (local dictionary) or both relevant and irrelevant feature set (universal dictionary). In this paper, we explored the different frameworks of feature selection to the task of text categorization on the Reuters(10) and Reuters(115) datasets (variants of Reuters-21578 corpus). We then investigate the efficiency of 7 different local or global feature selections corresponds the use of local and universal dictionary. Our experiments have shown that local feature selection with local dictionary yields optimal categorization results.
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paper_title: Text classification based on multi-word with support vector machine

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One of the main themes which support text mining is text representation; that is, its task is to look for appropriate terms to transfer documents into numerical vectors. Recently, many efforts have been invested on this topic to enrich text representation using vector space model (VSM) to improve the performances of text mining techniques such as text classification and text clustering. The main concern in this paper is to investigate the effectiveness of using multi-words for text representation on the performances of text classification. Firstly, a practical method is proposed to implement the multi-word extraction from documents based on the syntactical structure. Secondly, two strategies as general concept representation and subtopic representation are presented to represent the documents using the extracted multi-words. In particular, the dynamic k-mismatch is proposed to determine the presence of a long multi-word which is a subtopic of the content of a document. Finally, we carried out a series of experiments on classifying the Reuters-21578 documents using the representations with multi-words. We used the performance of representation in individual words as the baseline, which has the largest dimension of feature set for representation without linguistic preprocessing. Moreover, linear kernel and non-linear polynomial kernel in support vector machines (SVM) are examined comparatively for classification to investigate the effect of kernel type on their performances. Index terms with low information gain (IG) are removed from the feature set at different percentages to observe the robustness of each classification method. Our experiments demonstrate that in multi-word representation, subtopic representation outperforms the general concept representation and the linear kernel outperforms the non-linear kernel of SVM in classifying the Reuters data. The effect of applying different representation strategies is greater than the effect of applying the different SVM kernels on classification performance. Furthermore, the representation using individual words outperforms any representation using multi-words. This is consistent with the major opinions concerning the role of linguistic preprocessing on documents' features when using SVM for text classification.
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paper_title: Text classification from unlabeled documents with bootstrapping and feature projection techniques

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Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for accurate learning. While unlabeled documents are easily collected and plentiful, labeled documents are difficultly generated because a labeling task must be done by human developers. In this paper, we propose a new text classification method based on unsupervised or semi-supervised learning. The proposed method launches text classification tasks with only unlabeled documents and the title word of each category for learning, and then it automatically learns text classifier by using bootstrapping and feature projection techniques. The results of experiments showed that the proposed method achieved reasonably useful performance compared to a supervised method. If the proposed method is used in a text classification task, building text classification systems will become significantly faster and less expensive.
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paper_title: Machine learning in automated text categorization

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The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
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paper_title: Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks

paper_content:

Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. Recently, following the rising interest towards the Support Vector Machine, various studies showed that SVM outperforms other classification algorithms. So should we just not bother about other classification algorithms and opt always for SVM ?
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paper_title: Performance Comparative in Classification Algorithms Using Real Datasets

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Classification is one of the most common data mining tasks, used frequently for data categorization and analysis in the industry and research. In real-world data mining sometimes it mainly deals with noisy information sources, because of data collection inaccuracy, device limitations, data transmission and discretization errors, or man-made perturbations frequently result in imprecise or vague data which is called as noisy data. This noisy data may decrease performance of any classification algorithms. This paper deals with the performance of different classification algorithms and the impact of feature selection algorithm on Logistic Regression Classifier, How it controls False Discovery Rate (FDR) and thus improves the efficiency of Logistic Regression classifier.
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paper_title: Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks

paper_content:

Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. Recently, following the rising interest towards the Support Vector Machine, various studies showed that SVM outperforms other classification algorithms. So should we just not bother about other classification algorithms and opt always for SVM ?
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paper_title: Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup

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Motivation: The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases. Text mining may provide useful tools to assist in the curation process. To date, the lack of standards has made it impossible to determine whether text mining techniques are sufficiently mature to be useful. Results: We report on a Challenge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup. We provided a training corpus of 862 articles consisting of journal articles curated in FlyBase, along with the associated lists of genes and gene products, as well as the relevant data fields from FlyBase. For the test, we provided a corpus of 213 new (‘blind’) articles; the 18 participating groups provided systems that flagged articles for curation, based on whether the article contained experimental evidence for gene expression products. We report on the evaluation results and describe the techniques used by the top performing groups.
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paper_title: MSVM-kNN: Combining SVM and k-NN for Multi-class Text Classification

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Text categorization is the process of assigning documents to a set of previously fixed categories. It is widely used in many data-oriented management applications. Many popular algorithms for text categorization have been proposed, such as Naive Bayes, k-Nearest Neighbor (k-NN), Support Vector Machine (SVM). However, those classification approaches do not perform well in every case, for example, SVM can not identify categories of documents correctly when the texts are in cross zones of multi-categories, k-NN cannot effectively solve the problem of overlapped categories borders. In this paper, we propose an approach named as Multi-class SVM-kNN (MSVM-kNN) which is the combination of SVM and k-NN. In the approach, SVM is first used to identify category borders, then k-NN classifies documents among borders. MSVM-kNN can overcome the shortcomings of SVM and k-NN and improve the performance of multi-class text classification. The experimental results show MSVM-kNN performs better than SVM or kNN.
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Title: A Review of Machine Learning Algorithms for Text-Documents Classification
Section 1: INTRODUCTION
Description 1: Write an introduction discussing the importance of text mining due to the proliferation of electronic documents from various sources, the challenges of proper annotation, representation, and classification, and the key points in text classification.

Section 2: DOCUMENTS REPRESENTATION
Description 2: Discuss the preprocessing techniques of document representation, including feature extraction and feature selection, and how these techniques reduce document complexity and high dimensionality.

Section 3: Feature Extraction
Description 3: Describe the processes involved in feature extraction such as tokenization, stop words removal, and stemming, and the importance of these steps in document preprocessing.

Section 4: Feature Selection
Description 4: Explain the different methods for feature selection, their importance in improving the efficiency and accuracy of text classifiers, and examples of recent advancements in this area.

Section 5: Semantic and Ontology Base Documents Representation
Description 5: Explore the role of semantic and ontology-based techniques in enhancing text document representation and classification, and discuss the challenges and recent research in this field.

Section 6: MACHINE LEARNING TECHNIQUES
Description 6: Provide an overview of various supervised, unsupervised, and semi-supervised machine learning techniques for text classification, including popular algorithms and their characteristics.

Section 7: Rocchio's Algorithm
Description 7: Explain how Rocchio's Algorithm works, its benefits, limitations, and use cases in text classification.

Section 8: K-nearest neighbor (k-NN)
Description 8: Describe the k-nearest neighbor algorithm, its mechanism for determining document similarity, advantages, and challenges in application.

Section 9: Decision Tree
Description 9: Discuss the decision tree classifier, its structure, advantages, limitations, and performance in text classification tasks.

Section 10: Decision Rules Classification
Description 10: Outline how decision rules classification works, the algorithm involved, and its pros and cons in text classification.

Section 11: Naïve Bayes Algorithm
Description 11: Explain the Naïve Bayes classifier, its probabilistic approach, assumptions, advantages, and limitations.

Section 12. Artificial Neural Network
Description 12: Describe the implementation of artificial neural networks in text classification, including types, advantages, and computational costs.

Section 13: Fuzzy Correlation
Description 13: Discuss the use of fuzzy correlation in text classification and how it handles fuzzy or incomplete data.

Section 14: Genetic Algorithm
Description 14: Explain how genetic algorithms work in optimizing text classification parameters and improving classification accuracy.

Section 15: Support Vector Machine (SVM)
Description 15: Describe the SVM classification method, its principles, advantages in text classification, and challenges such as parameter tuning.

Section 16: HYBRID TECHNIQUES
Description 16: Provide an overview of hybrid classification methods combining multiple machine learning algorithms to improve classification accuracy and performance.

Section 17: COMPARATIVE STUDY
Description 17: Compare different text classification methods, their strengths, weaknesses, performance, and suitability for various applications, drawing on existing research.

Section 18: DISCUSSION AND CONCLUSIONS
Description 18: Summarize the review, including key observations, future work opportunities, and potential research areas in text document classification and machine learning.