Kernel modified optimal margin distribution machine for imbalanced data classification

Abstract

Although the optimal margin distribution machine (ODM) has better generalization performance in pattern recognition than traditional classifiers, ODM as well as traditional classifiers often suffers from data imbalance. To address this, this paper proposes a kernel modified ODM (KMODM) to eliminate the side effect of imbalanced data. According to the mechanism of ODM, a novel conformal function is designed to scale the kernel matrix of ODM, this can increases the separability of the training data in the feature space. In addition, to eliminate the skew of the separator toward minority class, KMODM introduces two free parameters in conformal function to balance the influence of different training data on separating hyperplane. Experimental results on two-dimensional visualization data show that KMODM can alleviate the skew of the separating hyperplane caused by imbalanced data. For most of ten UCI data sets, KMODM can broad the margin of the minority class and achieve the highest average G-mean and F1 score. This means that KMODM has more balanced detection rate and better generalization performance compared to other baseline methods, especially in presence of heavily imbalanced training data.