Feature Analysis for Imbalanced Learning

Abstract

Based on the results of artificial samples generated in the minority class and through the label regulation of the neighbor samples of the majority class, the precision of the classification prediction for imbalanced learning has clearly been enhanced. This article presents a unified solution combining learning factors to improve the learning performance. The proposed method solves this imbalance through a feature selection incorporating the generation of artificial samples and label regulation. A probabilistic representation is used for all aspects of learning: class, sample, and feature. A Bayesian inference is applied to the learning model to interpret the imbalance occurring in the training data and to describe solutions for recovering the balance. We show that the generation of artificial samples is sample based approach and label regulation is class based approach. We discovered that feature selection achieves surprisingly good results when combined with a sample- or class-based solution.