In binary classification problems it is common for the two classes to be imbalanced: one case is very rare compared to the other. Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly. In contrast, the algorithm infinitely imbalanced logistic regression (IILR) algorithm explicitly addresses class imbalance in its formulation. This p...

Imbalanced data, where the number of instances of one class is much higher than the others, are frequent in many domains such as fraud detection, telecommunications management, oil spill detection and text classification. Traditional classifiers do not perform well when considering data that are susceptible to both within-class and between-class imbalances. In this paper, we propose the ClustFi...

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is appli...

The class imbalance problem has been reported as an important challenge in various fields such as Pattern Recognition, Data Mining and Machine Learning. A less explored research area is related to how to evaluate classifiers on imbalanced data sets. This work analyzes the behaviour of performance measures widely used on imbalanced problems, as well as other metrics recently proposed in the lite...

In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced data sets and their broad application domains in data mining, and then summarizes the evaluation metrics and the existing methods to evaluate and solve the imbalance problem. Synthetic minority oversampling technique (S...

A class-imbalanced classifier is a decision rule to predict the class membership of new samples from an available data set where the class sizes differ considerably. When the class sizes are very different, most standard classification algorithms may favor the larger (majority) class resulting in poor accuracy in the minority class prediction. A class-imbalanced classifier typically modifies a ...

The class imbalance problem has been reported as an important challenge in various fields such as Pattern Recognition, Data Mining and Machine Learning. A less explored research area is related to how to evaluate classifiers on imbalanced data sets. This work analyzes the behaviour of performance measures widely used on imbalanced problems, as well as other metrics recently proposed in the lite...

The acquisition of face images is usually limited due to policy and economy considerations, and hence the number of training examples of each subject varies greatly. The problem of face recognition with imbalanced training data has drawn attention of researchers and it is desirable to understand in what circumstances imbalanced data set affects the learning outcomes, and robust methods are need...

Most existing classification approaches assume the underlying training set is evenly distributed. In class imbalanced classification, the training set for one class (majority) far surpassed the training set of the other class (minority), in which, the minority class is often the more interesting class. In this paper, we review the issues that come with learning from imbalanced class data sets a...

In this contribution we carry out an analysis of the rule weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of imbalanced data-sets with a high imbalance degree. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best configuration of rule weight and Fuzzy Reasoning Method also studying the cooperation of some...