A key issue in machine learning is the ability to cope with recognition problems where one or more classes are under-represented with respect to the others. Indeed, traditional algorithms fail under class imbalanced distribution resulting in low predictive accuracy over the minority classes. While large literature exists on binary imbalanced tasks, few researches exist for multiclass learning. ...

Classification with imbalanced data-sets supposes a new challenge for researches in the framework of data mining. This problem appears when the number of examples that represents one of the classes of the data-set (usually the concept of interest) is much lower than that of the other classes. In this manner, the learning model must be adapted to this situation, which is very common in real appl...

Nowadays, many real applications comprise data-sets where the distribution of the classes is significantly different. These data-sets are commonly known as imbalanced data-sets. Traditional classifiers are not able to deal with these kinds of data-sets because they tend to classify only majority classes, obtaining poor results for minority classes. The approaches that have been proposed to addr...

Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been reported to yield promising results. However, the majority of ensemble metho...

Classification of imbalanced datasets is one of the widely explored challenges of the decade. The imbalance occurs in many real world datasets due to uneven distribution of data into classes, i.e. one class has more instances while others have a few that results in the biased performances of traditional classifiers towards the majority class with large number of instances and ignorance of other...

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...

In imbalanced multi-class classification problems, the misclassification rate as an error measure may not be a relevant choice. Several methods have been developed where the performance measure retained richer information than the mere misclassification rate: misclassification costs, ROC-based information, etc. Following this idea of dealing with alternate measures of performance, we propose to...

In imbalanced multi-class classification problems, the misclassification rate as an error measure may not be a relevant choice. Several methods have been developed where the performance measure retained richer information than the mere misclassification rate: misclassification costs, ROC-based information, etc. Following this idea of dealing with alternate measures of performance, we propose to...

A dataset is said to be imbalanced when its classes are disproportionately represented in terms of the number of instances they contain. This problem is common in applications such as medical diagnosis of rare diseases, detection of fraudulent calls, signature recognition. In this paper we propose an alternative method for imbalanced learning, which balances the dataset using an undersampling s...

This papers deals with inducing rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining classes (majority classes). We discuss reasons for bias of standard classifiers toward recognition of examples from majority classes and misclassifcation of the minority class. To avoid limitations of sequential covering approaches, ...