An overview of rough-hybrid approaches in image processing
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paper_title: An Automatic Classification System Applied in Medical Images

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In this paper, a multi-class classification system is developed for medical images. We have mainly explored ways to use different image features, and compared two classifiers: Principle Component Analysis (PCA) and Supporting Vector Machines (SVM) with RBF (radial basis functions) kernels. Experimental results showed that SVM with a combination of the middle-level blob feature and low-level features (down-scaled images and their texture maps) achieved the highest recognition accuracy. Using the 9000 given training images from ImageCLEF05, our proposed method has achieved a recognition rate of 88.9% in a simulation experiment. And according to the evaluation result from the ImageCLEF05 organizer, our method has achieved a recognition rate of 82% over its 1000 testing images.
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paper_title: Rough Sets: Mathematical Foundations

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1 Rough Set Theory: An Introduction.- 2 The Sentential Calculus.- 3 Logical Theory of Approximations.- 4 Many-Valued Sentential Calculi.- 5 Propositional Modal Logic.- 6 Set Theory.- 7 Topological Structures.- 8 Algebraic Structures.- 9 Predicate Calculus.- 10 Independence, Approximation.- 11 Topology of Rough Sets.- 12 Algebra and Logic of Rough Sets.- 13 Infinite-valued Logical Calculi.- 14 From Rough to Fuzzy.- List of Symbols.
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paper_title: Classification of objects by means of attributes

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W: ICS PAS Reports 429/81, pages 1-20. Institute of Computer Science Polish Academy of Sciences (ICS PAS), Warsaw, Poland, 1981
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paper_title: Rough Sets and Boolean Reasoning

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Abstract In this article, we discuss methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis.
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paper_title: Remote Sensing Image Classification Algorithm Based on Rough Set Theory

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Rough sets theory is a relatively new soft computing tool to deal with vagueness and uncertainty. Considering the feature of remote sensing images and the basic theory and applications of rough sets, we put forward a remote sensing image classification algorithm based on rough set theory. In this article we first introduce the basic theory and character of rough sets and its applications in recent years are also pointed out. Then the theory of rough sets is introduced into the processing of remote image classify. Experiment research and classification effects are showed in this article about the new technology and it seems innovational and useful.
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paper_title: Rudiments of rough sets

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Abstract Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough set-based research directions and applications.
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paper_title: A rough sets approach of hyperspectral image classification

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Rough set theory has a powerful capability for attributes reduction and classification rules extraction, while artificial neural network (ANN) performances well in classification problems with a satisfactory accuracy. In this paper we focus our attention to investigate a way of integrating rough set theory and multi layer perceptron (MLP) in soft computing paradigm for classification and rule generation of hyperspectral remote sensing image classification. The novelty of this method lies in applying rough set theory for extracting classification rules and computing fuzzy membership values directly from decision table after attributes reduction on a real-valued attribute table consisting of classification features. The successful application of this approach in hyperspectral remote sensing images mineral classification illustrates the flexibility and practicality of this new approach.
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paper_title: Rough sets

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Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
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paper_title: Rough Sets: Mathematical Foundations

paper_content:

1 Rough Set Theory: An Introduction.- 2 The Sentential Calculus.- 3 Logical Theory of Approximations.- 4 Many-Valued Sentential Calculi.- 5 Propositional Modal Logic.- 6 Set Theory.- 7 Topological Structures.- 8 Algebraic Structures.- 9 Predicate Calculus.- 10 Independence, Approximation.- 11 Topology of Rough Sets.- 12 Algebra and Logic of Rough Sets.- 13 Infinite-valued Logical Calculi.- 14 From Rough to Fuzzy.- List of Symbols.
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paper_title: Using Rough Sets as Tools for Knowledge Discovery

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An attribute-oriented rough set method for knowledge discovery in databases is described. The method is based on information generalization, which examines the data at various levels of abstraction, followed by the discovery, analysis and simplification of significant data relationships. First, an attribute-oriented concept tree ascension technique is applied to generalize the information; this step substantially reduces the overall computational cost. Then rough set techniques are applied to the generalized information system to derive rules. The rules represent data dependencies occurring in the database. We focus on discovering hidden patterns in the database rather than statistical summaries.
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paper_title: Rough sets

paper_content:

Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
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paper_title: On links between mathematical morphology and rough sets

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Abstract Based on the observation that rough sets and mathematical morphology are both using dual operators sharing similar properties, we investigate more closely the links existing between both the domains. We establish the equivalence between some morphological operators and rough sets defined from either a relation, or a pair of dual operators or a neighborhood system. Then we suggest some extensions using morphological thinning and thickening, and using algebraic operators. We propose to define rough functions and fuzzy rough sets using mathematical morphology on functions and fuzzy mathematical morphology.
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paper_title: Structure optimization of wavelet neural network using rough set theory

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This paper presents an approach to minimize the redundancy of structure existing in frame-based wavelet neural networks using the rough sets theory. The original structure of the wavelet network is obtained through a time-frequency analysis. Then the redundant nodes are eliminated in light of the dependency between the output of the network and nodes in the hidden layers to optimize the structure of the wavelet network. Simulation results show the proposed method is simple and effective.
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paper_title: Relations in Mathematical Morphology with applications to Graphs and Rough Sets

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Rough sets have been applied in spatial information theory to construct theories of granularity - presenting information at different levels of detail. Mathematical morphology can also be seen as a framework for granularity, and the question of how rough sets relate to mathematical morphology has been raised by Bloch. This paper shows how by developing mathematical morphology in terms of relations we obtain a framework which includes the basic constructions of rough set theory as a special case. The extension of the relational framework to mathematical morphology on graphs rather than sets is explored and new operations of dilations and erosions on graphs are obtained.
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paper_title: The investigation of the Bayesian rough set model

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The original Rough Set model is concerned primarily with algebraic properties of approximately defined sets. The Variable Precision Rough Set (VPRS) model extends the basic rough set theory to incorporate probabilistic information. The article presents a non-parametric modification of the VPRS model called the Bayesian Rough Set (BRS) model, where the set approximations are defined by using the prior probability as a reference. Mathematical properties of BRS are investigated. It is shown that the quality of BRS models can be evaluated using probabilistic gain function, which is suitable for identification and elimination of redundant attributes.
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paper_title: Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

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The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant and benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest (ROI) and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the ROI by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different ROI to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.
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paper_title: Morphological Image Analysis: Principles and Applications

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From the Publisher: ::: The purpose of this book is to provide readers with an in-depth presentation of the principles and applications of morphological image analysis. This is achieved through a step by step process starting from the basic morphological operators and extending to the most recent advances which have proven their practical usefulness. This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.
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paper_title: Rough Sets and Bayesian Methods Applied to Cancer Detection

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The paper describes an application of soft computing methods of rough sets and Bayesian inference to a breast cancer detection using electro-potentials. The statistical principal component analysis (PCA) and the rough sets methods were applied for feature extraction, reduction and selection. The quadratic discriminant was applied as a classifier for a breast cancer detection.
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paper_title: Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network

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The main aim of this paper is to provide an accurate boundary detection algorithm of the prostate ultrasound images to assist radiologists in making their decisions. To increase the contrast of the ultrasound prostate image, the intensity values of the original images were adjusted firstly using the PCNN with median filter. It is followed by the PCNN segmentation algorithm to detect the boundary of the image. Combining adjusting and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Analysis and experimental results show that the best segmentation output can be drawn from the simple and sophisticated ultrasound images using the spiking neural networks.
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paper_title: Image fusion algorithm using rough sets theory and wavelet analysis

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Wavelets with their multiresolution property, have been proved to be effective in the integrating of the coarse features and finer resolution details of source images to produce a good fused image. The theory of rough sets has emerged as a major mathematical approach for managing uncertainty that arises from inexact, noisy, or incomplete information. Image fusion algorithm using rough sets and wavelet analysis is proposed in this paper. Multifocus images are enhanced using rough sets, then fused them using Db4 wavelet. The proposed fusion scheme is examined on images which are contaminated by salt-pepper noise. Entropy is used to evaluate image quality. Experimental results demonstrate the effectiveness of the image fusion algorithm.
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paper_title: Analytical Morphology: Mathematical Morphology of Decision Tables

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We propose a method called analytical morphology for data filtering. The method was created on the basis of some ideas of rough set theory and mathematical morphology. Mathematical morphology makes an essential use of geometric structure of objects while the aim of our method is to provide tools for data filtering when there is no directly available geometric structure in the data set.
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paper_title: Application of Rough Sets in Binary Morphology

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Based on Rough Sets and Mathematical Morphology theory, this paper proposes a method called membership-grade operator for binary image processing. Based on the same structural element, the operator achieves multiform dilations, erosions and edge extractions by proper choice of membership grade obtained by binary image. Applied to noise image, the operator can achieve the above processing and noise reduction, and good effect is obtained by image simulation. Finally, this paper analyzes the characteristics of the membership-grade operator by comparing it with the morphology operator.
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paper_title: A Better Classifier Based on Rough Set and Neural Network for Medical Images

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Detecting tumor in mammography is a difficult task because of complexity in the image. This brings the necessity of creating automatic tools to find whether a mammography present tumor or not. In this paper we integrate neural network with reduction of rough set theory which we call the rough neural network (RNN) to classify digital mammography. The experimental results show that the RNN performs better than purely using neural network in terms of time, and it can get 92.37% classifying accuracy which is higher than 81.25% using neural network only.
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paper_title: Rough set algorithms in classification problem

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We we present some algorithms, based on rough set theory, that can be used for the problem of new cases classification. Most of the algorithms were implemented and included in Rosetta system [43]. We present several methods for computation of decision rules based on reducts. We discuss the problem of real value attribute discretization for increasing the performance of algorithms and quality of decision rules. Finally we deal with a problem of resolving conflicts between decision rules classifying a new case to different categories (classes). Keywords: knowledge discovery, rough sets, classification algorithms, reducts, decision rules, real value attribute discretization
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paper_title: Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer

paper_content:

The objective of this paper is to introduce a rough neural intelligent approach for rule generation and image classification. Hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify breast cancer images into two outcomes: malignant cancer or benign cancer. Algorithms based on fuzzy image processing are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. Then, the rough set approach to attribute reduction and rule generation is presented. Finally, rough neural network is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. Rough neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bounds. ::: ::: To evaluate performance of the presented rough neural approach, we run tests over different mammogram images. The experimental results show that the overall classification accuracy offered by rough neural approach is high compared with other intelligent techniques.
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paper_title: Rough Neural Computing in Signal Analysis

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This paper introduces an application of a particular form of rough neural computing in signal analysis. The form of rough neural network used in this study is based on rough sets, rough membership functions, and decision rules. Two forms of neurons are found in such a network: rough membership function neurons and decider neurons. Each rough membership function neuron constructs upper and lower approximation equivalence classes in response to input signals as an aid to classifying inputs. In this paper, the output of a rough membership function neuron results from the computation performed by a rough membership function in determining degree of overlap between an upper approximation set representing approximate knowledge about inputs and a set of measurements representing certain knowledge about a particular class of objects. Decider neurons implement granules derived from decision rules extracted from data sets using rough set theory. A decider neuron instantiates approximate reasoning in assessing rough membership function values gleaned from input data. An introduction to the basic concepts underlying rough membership neural networks is briefly given. An application of rough neural computing in classifying the power system faults is considered.
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paper_title: A Rough Set Based Model to Rank the Importance of Association Rules

paper_content:

Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based process by which a rule importance measure is calculated for association rules to select the most appropriate rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule among the rule sets. Rule importance is different from rule interestingness in that it does not consider the predefined knowledge on what kind of information is considered to be interesting. The experimental results show our method reduces the number of rules generated and at the same time provides a measure of how important is a rule.
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paper_title: Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach

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This paper introduces an approach to classifying faults in high voltage power system with a combination of rough sets and fuzzy sets in a neural computing framework. Typical error signals important for fault detection in power systems are considered. Features of these error signals derived earlier using Fast Fourier Transform analysis, amplitude estimation and waveform type identification, provide inputs to a neural network used in classifying faults. A form of rough neuron with memory is introduced in this paper. A brief overview of a rough-fuzzy neural computational method is given. The learning performance of a rough-fuzzy and pure fuzzy neural network are compared.
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paper_title: Rough sets as a front end of neural-networks texture classifiers

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Abstract The paper describes an application of rough sets method to feature selection and reduction as a front end of neural-network-based texture images recognition. The methods applied include singular-value decomposition (SVD) for feature extraction, principal components analysis (PCA) for feature projection and reduction, and rough sets methods for feature selection and reduction. For texture classification the feedforward backpropagation neural networks were applied. The numerical experiments show the ability of rough sets to select reduced set of pattern's features (minimizing the pattern size), while providing better generalization of neural-network texture classifiers.
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paper_title: Towards Rough Neural Computing Based on Rough Membership Functions: Theory and Application

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This paper introduces a neural network architecture based on rough sets and rough membership functions. The neurons of such networks instantiate approximate reasoning in assessing knowledge gleaned from input data. Each neuron constructs upper and lower approximations as an aid to classifying inputs. Rough neuron output has various forms. I n this paper, rough neuron output results from the application of a rough membership function. A brief introduction to the basic concepts underlying rough membership neural networks i s g iven. A n application of rough neural computing is briefly considered in classifying the waveforms of power system faults. Experimental results with rough neural classification of waveforms are also given.
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paper_title: Vocabulary Mining for Information Retrieval: Rough Sets and Fuzzy Sets

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Abstract Vocabulary mining in information retrieval refers to the utilization of the domain vocabulary towards improving the user’s query. Most often queries posed to information retrieval systems are not optimal for retrieval purposes. Vocabulary mining allows one to generalize, specialize or perform other kinds of vocabulary-based transformations on the query in order to improve retrieval performance. This paper investigates a new framework for vocabulary mining that derives from the combination of rough sets and fuzzy sets. The framework allows one to use rough set-based approximations even when the documents and queries are described using weighted, i.e., fuzzy representations. The paper also explores the application of generalized rough sets and the variable precision models. The problem of coordination between multiple vocabulary views is also examined. Finally, a preliminary analysis of issues that arise when applying the proposed vocabulary mining framework to the Unified Medical Language System (a state-of-the-art vocabulary system) is presented. The proposed framework supports the systematic study and application of different vocabulary views in information retrieval.
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paper_title: Rough fuzzy set based scale space transforms and their use in image analysis

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In this paper we present a multi-scale method based on the hybrid notion of rough fuzzy sets, coming from the combination of two models of uncertainty like vagueness by handling rough sets and coarseness by handling fuzzy sets. Marrying both notions lead to consider, as instance, approximation of sets by means of similarity relations or fuzzy partitions. The most important features are extracted from the scale spaces by unsupervised cluster analysis, to successfully tackle image processing tasks. Here, we report some results achieved by applying the method to multi-class image segmentation and edge detection, but it can be shown to be successfully applied to texture discrimination problem too.
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paper_title: Classification of multispectral images through a rough-fuzzy neural network

paper_content:

A new fuzzy Hopfield-model net based on rough-set reason- ing is proposed for the classification of multispectral images. The main purpose is to embed a rough-set learning scheme into the fuzzy Hopfield network to construct a classification system called a rough-fuzzy Hopfield net (RFHN). The classification system is a paradigm for the implementation of fuzzy logic and rough systems in neural network ar- chitecture. Instead of all the information in the image being fed into the neural network, the upper- and lower-bound gray levels, captured from a training vector in a multispectal image, are fed into a rough-fuzzy neuron in the RFHN. Therefore, only 2/N pixels are selected as the training samples if an N-dimensional multispectral image was used. In the simu- lation results, the proposed network not only reduces the consuming time but also reserves the classification performance. © 2004 Society of
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paper_title: Fuzzy-Rough set based nearest neighbor clustering classification algorithm

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We propose a new nearest neighbor clustering classification algorithm based on fuzzy-rough set theory (FRNNC). First, we make every training sample fuzzy-roughness and use edit nearest neighbor algorithm to remove training sample points in class boundary or overlapping regions, and then use Mountain Clustering method to select representative cluster center points, then Fuzzy-Rough Nearest neighbor algorithm (FRNN) is applied to classify the test data. The new algorithm is applied to hand gesture image recognition, the results show that it is more effective and performs better than other nearest neighbor methods.
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paper_title: Evolutionary Modular MLP with Rough Sets and ID3 Algorithm for Staging of Cervical Cancer

paper_content:

This paper describes a way of designing a hybrid system for detecting the different stages of cervical cancer. Hybridisation includes the evolution of knowledge-based subnetwork modules with GAs using rough set theory and the ID3 algorithm. Crude subnetworks for each module are initially obtained via rough set theory and the ID3 algorithm. These subnetworks are then combined, and the final network is evolved using genetic algorithms. The evol-ution uses a restricted mutation operator which utilises the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conven-tional MLP. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for rule extraction.
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paper_title: Matching 2d image segments with genetic algorithms and approximation spaces

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This article introduces an approach to matching 2D image segments using approximation spaces. The rough set approach introduced by Zdzislaw Pawlak provides a ground for concluding to what degree a particular set of similar image segments is a part of a set of image segments representing a norm or standard. The number of features (color difference and overlap between segments) typically used to solve the image segment matching problem is small. This means that there is not enough information to permit image segment matching with high accuracy. By contrast, many more features can be used in solving the image segment matching problem using a combination of evolutionary and rough set methods. Several different uses of a Darwinian form of a genetic algorithm (GA) are introduced as a means to partition large collections of image segments into blocks of similar image segments. After filtering, the output of a GA provides a basis for finding matching segments in the context of an approximation space. A coverage form of approximation space is presented in this article. Such an approximation space makes it possible to measure the the extent that a set of image segments representing a standard covers GA-produced blocks. The contribution of this article is the introduction of an approach to matching image segments in the context of an approximation space.
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paper_title: Unsupervised Rough Set Classification Using GAs

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The rough set is a useful notion for the classification of objects when the available information is not adequate to represent classes using precise sets. Rough sets have been successfully used in information systems for learning rules from an expert. This paper describes how genetic algorithms can be used to develop rough sets. The proposed rough set theoretic genetic encoding will be especially useful in unsupervised learning. A rough set genome consists of upper and lower bounds for sets in a partition. The partition may be as simple as the conventional expert class and its complement or a more general classification scheme. The paper provides a complete description of design and implementation of rough set genomes. The proposed design and implementation is used to provide an unsupervised rough set classification of highway sections.
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paper_title: Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

paper_content:

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min–Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.
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paper_title: A Hybrid Rough Set--Particle Swarm Algorithm for Image Pixel Classification

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This article presents a framework to hybridize the rough set theory with a famous swarm intelligence algorithm known as Particle Swarm Optimization (PSO). The hybrid rough-PSO technique has been used for grouping the pixels of an image in its intensity space. Medical and remote sensing satellite images become corrupted with noise very often. Fast and efficient segmentation of such noisy images (which is essential for their further interpretation in many cases) has remained a challenging problem for years. In this work, we treat image segmentation as a clustering problem. Each cluster is modeled with a rough set. PSO is employed to tune the threshold and relative importance of upper and lower approximations of the rough sets. Davies-Bouldin clustering validity index is used as the fitness function, which is minimized while arriving at an optimal partitioning.
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paper_title: Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification

paper_content:

Support vector machines (SVMs) are essentially binary classifiers. To improve their applicability, several methods have been suggested for extending SVMs for multi-classification, including one-versus-one (1-v-1), one-versus-rest (1-v-r) and DAGSVM. In this paper, we first describe how binary classification with SVMs can be interpreted using rough sets. A rough set approach to SVM classification removes the necessity of exact classification and is especially useful when dealing with noisy data. Next, by utilizing the boundary region in rough sets, we suggest two new approaches, extensions of 1-v-r and 1-v-1, to SVM multi-classification that allow for an error rate. We explicitly demonstrate how our extended 1-v-r may shorten the training time of the conventional 1-v-r approach. In addition, we show that our 1-v-1 approach may have reduced storage requirements compared to the conventional 1-v-1 and DAGSVM techniques. Our techniques also provide better semantic interpretations of the classification process. The theoretical conclusions are supported by experimental findings involving a synthetic dataset.
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paper_title: Facial expression recognition based on rough set theory and SVM

paper_content:

Facial expression recognition is becoming more and more important in computer application, such as health care, children education, etc. Based on geometric feature and appearance feature, there are a few works have been done on facial expression recognition using such methods as ANN, SVM, etc. In this paper, considering geometric feature only, a novel approach based on rough set theory and SVM is proposed. The experiment results show this approach can get high recognition ratio and reduce the cost of calculation
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paper_title: An Improved SVM Classifier for Medical Image Classification

paper_content:

Support Vector Machine (SVM) has high classifying accuracy and good capabilities of fault-tolerance and generalization. The Rough Set Theory (RST) approach has the advantages on dealing with a large amount of data and eliminating redundant information. In this paper, we join SVM classifier with RST which we call the Improved Support Vector Machine (ISVM) to classify digital mammography. The experimental results show that this ISVM classifier can get 96.56% accuracy which is higher about 3.42% than 92.94% using SVM, and the error recognition rates are close to 100% averagely.
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paper_title: A hybrid classifier based on rough set theory and support vector machines

paper_content:

Rough set theory (RST) can mine useful information from a large number of data and generate decision rules without prior knowledge. Support vector machines (SVMs) have good classification performances and good capabilities of fault-tolerance and generalization. To inherit the merits of both RST and SVMs, a hybrid classifier called rough set support vector machines (RS-SVMs) is proposed to recognize radar emitter signals in this paper. RST is used as preprocessing step to improve the performances of SVMs. A large number of experimental results show that RS-SVMs achieve lower recognition error rates than SVMs and RS-SVMs have stronger capabilities of classification and generalization than SVMs, especially when the number of training samples is small. RS-SVMs are superior to SVMs greatly.
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Title: An Overview of Rough-Hybrid Approaches in Image Processing

Section 1: INTRODUCTION
Description 1: Provide an overview of rough set theory and its application in image processing, including its integration with other computational intelligence techniques.

Section 2: ROUGH SET: AN OVERVIEW
Description 2: Discuss the fundamentals of rough set theory, including definitions, key concepts, and properties.

Section 3: ROUGH SET-NEURAL NETWORK APPROACHES IN IMAGE PROCESSING
Description 3: Explain how rough sets are combined with neural networks for tasks such as image classification and rule generation.

Section 4: ROUGH SET-FUZZY SET APPROACHES IN IMAGE PROCESSING
Description 4: Describe the integration of rough sets with fuzzy sets and their application in areas like image classification and segmentation.

Section 5: ROUGH SET-GENETIC ALGORITHM APPROACHES IN IMAGE PROCESSING
Description 5: Explore the combination of rough sets and genetic algorithms in image processing applications such as unsupervised classification.

Section 6: ROUGH SET-SWARM INTELLIGENCE APPROACHES IN IMAGE PROCESSING
Description 6: Detail the use of rough sets with swarm intelligence algorithms such as Particle Swarm Optimization (PSO) for tasks like image segmentation.

Section 7: ROUGH SET-SUPPORT VECTOR MACHINE APPROACHES IN IMAGE PROCESSING
Description 7: Discuss the combination of rough sets with support vector machines (SVMs) for improved image recognition and classification.

Section 8: CONCLUSIONS
Description 8: Summarize the various rough-hybrid approaches discussed and emphasize their effectiveness in enhancing image processing tasks.