In this paper we investigate the suitability of a fuzzy system as a classifier for imbalanced data problems. Primarily, the fuzzy model performance is evaluated on artificial data sets, generated with various levels of size, complexity and imbalance. It is investigated what combination of the three problematic issues makes the learning problem harder [4]. A theoretic analysis shows that for a f...

Recently, the class imbalance problem in neural networks, is receiving growing attention in works of machine learning and data mining. This problem appears when the samples of some classes are much smaller than those in the other classes. The classes with small size can be ignored in the learning process and the convergence of these classes is very slow. This paper studies empirically the class...

Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue, conventional methods tend to be biased toward the majority class with poor predictive accuracy for the minority class. These methods further deteriorate on small, imbalanced data that have a large degree of class overlap. In this paper, we propose a novel discriminative sparse neighb...

Class imbalance problem is one of the greatest challenges in machine learning and data mining researches, which has acquired significant research interest from academics, industries and research teams in recent years. Researchers have proposed many techniques to handle the class imbalance problem, including resampling, new algorithms, and feature selection. The class imbalance problem is even m...

It has long been recognized that phylogenetic trees are more unbalanced than those generated by a Yule process. Recently, the degree of this imbalance has been quantified using the large set of phylogenetic trees available in the TreeBASE data set. In this article, a more precise analysis of imbalance is undertaken. Trees simulated under a range of models are compared with trees from TreeBASE a...

Although class imbalance learning and online learning have been extensively studied in the literature separately, online class imbalance learning that considers the challenges of both ̄elds has not drawn much attention. It deals with data streams having very skewed class distributions, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. T...