Entropy-based matrix learning machine for imbalanced data sets

Imbalance problem occurs when negative class contains many more patterns than that of positive class. Since conventional Support Vector Machine (SVM) and Neural Networks (NN) have been proven not to effectively handle imbalanced data, some improved learning machines including Fuzzy SVM (FSVM) have been proposed. FSVM applies a fuzzy membership to each training pattern such that different patterns can give different contributions to the learning machine. However, how to evaluate fuzzy membership becomes the key point to FSVM. Moreover, these learning machines present disadvantages to process matrix patterns. In order to process matrix patterns and to tackle the imbalance problem, this paper proposes an entropy-based matrix learning machine for imbalanced data sets, adopting the Matrix-pattern-oriented Ho-Kashyap learning machine with regularization learning (MatMHKS) as the base classifier. The new leaning machine is named EMatMHKS and its contributions are: (1) proposing a new entropy-based fuzzy membership evaluation approach which enhances the importance of patterns, (2) guaranteeing the importance of positive patterns and get a more flexible decision surface. Experiments on real-world imbalanced data sets validate that EMatMHKS outperforms compared learning machines.