AN OVERVIEW ON DATA MINING DESIGNED FOR IMBALANCED DATASETS
---
paper_title: Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction

paper_content:

For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the training examples and/or the computational costs associated with learning from them. In such circumstances, one question of practical importance is: if only n training examples can be selected, in what proportion should the classes be represented? In this article we help to answer this question by analyzing, for a fixed training-set size, the relationship between the class distribution of the training data and the performance of classification trees induced from these data. We study twenty-six data sets and, for each, determine the best class distribution for learning. The naturally occurring class distribution is shown to generally perform well when classifier performance is evaluated using undifferentiated error rate (0/1 loss). However, when the area under the ROC curve is used to evaluate classifier performance, a balanced distribution is shown to perform well. Since neither of these choices for class distribution always generates the best-performing classifier, we introduce a "budget-sensitive" progressive sampling algorithm for selecting training examples based on the class associated with each example. An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) classification performance.
---
paper_title: Inductive learning algorithms and representations for text categorization

paper_content:

Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.
---
paper_title: A multiple resampling method for learning from imbalanced data sets

paper_content:

Resampling methods are commonly used for dealing with the class-imbalance problem. Their advantage over other methods is that they are external and thus, easily transportable. Although such approaches can be very simple to implement, tuning them most effectively is not an easy task. In particular, it is unclear whether oversampling is more effective than undersampling and which oversampling or undersampling rate should be used. This paper presents an experimental study of these questions and concludes that combining different expressions of the resampling approach is an effective solution to the tuning problem. The proposed combination scheme is evaluated on imbalanced subsets of the Reuters-21578 text collection and is shown to be quite effective for these problems.
---
paper_title: A selective sampling method for imbalanced data learning on support vector machines

paper_content:

The class imbalance problem in classification has been recognized as a significant research problem in recent years and a number of methods have been introduced to improve classification results. Rebalancing class distributions (such as over-sampling or under-sampling of learning datasets) has been popular due to its ease of implementation and relatively good performance. For the Support Vector Machine (SVM) classification algorithm, research efforts have focused on reducing the size of learning sets because of the algorithm's sensitivity to the size of the dataset. In this dissertation, we propose a metaheuristic approach (Genetic Algorithm) for under-sampling of an imbalanced dataset in the context of a SVM classifier. The goal of this approach is to find an optimal learning set from imbalanced datasets without empirical studies that are normally required to find an optimal class distribution. Experimental results using real datasets indicate that this metaheuristic under-sampling performed well in rebalancing class distributions. Furthermore, an iterative sampling methodology was used to produce smaller learning sets by removing redundant instances. It incorporates informative and the representative under-sampling mechanisms to speed up the learning procedure for imbalanced data learning with a SVM. When compared with existing rebalancing methods and the metaheuristic approach to under-sampling, this iterative methodology not only provides good performance but also enables a SVM classifier to learn using very small learning sets for imbalanced data learning. For large-scale imbalanced datasets, this methodology provides an efficient and effective solution for imbalanced data learning with an SVM.
---
paper_title: Explicitly representing expected cost: an alternative to ROC representation

paper_content:

ABSTRACT This paper proposes an alternative to ROC representation, in which the expected cost of a classi er is represented explicitly. This expected cost representation maintains many of the advantages of ROC representation, but is easier to understand. It allows the experimenter to immediately see the range of costs and class frequencies where a particular classi er is the best and quantitatively how much better it is than other classi ers. This paper demonstrates there is a point/line duality between the two representations. A point in ROC space representing a classi er becomes a line segment spanning the full range of costs and class frequencies. This duality produces equivalent operations in the two spaces, allowing most techniques used in ROC analysis to be readily reproduced in the cost space.
---
paper_title: Minority report in fraud detection: classification of skewed data

paper_content:

This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partitions derived from minority oversampling with replacement. Its originality lies in the use of a single meta-classifier (stacking) to choose the best base classifiers, and then combine these base classifiers' predictions (bagging) to improve cost savings (stacking-bagging). Results from a publicly available automobile insurance fraud detection data set demonstrate that stacking-bagging performs slightly better than the best performing bagged algorithm, C4.5, and its best classifier, C4.5 (2), in terms of cost savings. Stacking-bagging also outperforms the common technique used in industry (BP without both sampling and partitioning). Subsequently, this paper compares the new fraud detection method (meta-learning approach) against C4.5 trained using undersampling, oversampling, and SMOTEing without partitioning (sampling approach). Results show that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions. The most interesting find is confirming that the combination of classifiers to produce the best cost savings has its contributions from all three algorithms.
---

Title: AN OVERVIEW ON DATA MINING DESIGNED FOR IMBALANCED DATASETS

Section 1: INTRODUCTION
Description 1: Introduce the problem of class imbalance in machine learning, its prevalence across various applications, and how it impacts the performance of learning algorithms.

Section 2: PROBLEMS IN IMBALANCE
Description 2: Discuss the significance of the class imbalance issue in different domains, and categorize the approaches to tackle this problem into internal (algorithm-specific) and external (data preprocessing) methods.

Section 3: SAMPLING STRATEGIES
Description 3: Describe various sampling methods used to address class imbalance, including under-sampling, over-sampling, and synthetic sample generation techniques like SMOTE.

Section 4: COST MATRIX
Description 4: Explain the concept of cost-sensitive learning, where different misclassification costs are considered, and how cost matrices are used to balance class distributions.

Section 5: METHODS OF ENSEMBLE BASED
Description 5: Outline the use of ensemble methods such as boosting and bagging to improve classifier performance on imbalanced datasets and detail how these methods integrate with sampling techniques.

Section 6: SMOTE Boost
Description 6: Present the SMOTE Boost algorithm, which combines SMOTE with boosting to improve the prediction accuracy for minority classes while maintaining overall performance.

Section 7: CONCLUSION
Description 7: Summarize the importance of addressing imbalanced datasets, suggest that data-level preprocessing is the most effective solution, and highlight the paper's contributions to understanding imbalance issues and their remedies.