Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exists many domains for which one class is represented by a large number of examples while the other is represented by only a few. The purpose of this paper is 1) to demonstrate experimentally that, at least in...

Support Vector Machines (SVMs) have strong theoretical foundations and excellent empirical success in many pattern recognition and data mining applications. However, when induced by imbalanced training sets, where the examples of the target class (minority) are outnumbered by the examples of the non-target class (majority), the performance of SVM classifier is not so successful. In medical diag...

In this paper, a novel k -nearest neighbors (kNN) weighting strategy is proposed for handling the problem of class imbalance. When dealing with highly imbalanced data, a salient drawback of existing kNN algorithms is that the class with more frequent samples tends to dominate the neighborhood of a test instance in spite of distance measurements, which leads to suboptimal classification performa...

In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CORBFN, a cooperative-competitive evolu...

1 Introduction The class imbalance problem is a challenge to machine learning and data mining, and it has attracted significant research recent years. A classifier affected by the class imbalance problem for a specific data set would see strong accuracy overall but very poor performance on the minority class. The imbalance data sets are pervasive in real-world applications. Examples of these ki...

Imbalanced datasets are datasets with different samples distribution in which the distribution of samples in one class is scientifically more than other class samples. Learning a classification model for such imbalanced data has been shown to be a tricky task. In this paper we will focus on learning classifier systems, and will suggest a new XCS-based approach for learning classification models...

credit scoring is a classification problem leading to introducing numerous techniques to deal with it such as support vector machines, neural networks and rule-based classifiers. rule bases are the top priority in credit decision making because of their ability to explicitly distinguish between good and bad applicants.in a credit- scoring context, imbalanced data sets frequently occur as the nu...