Dynamic feature weighting for imbalanced data sets

Abstract

Most of data mining algorithms including classifiers suffer from data sets with highly imbalanced distribution of the target variable. The problem becomes more serious when the events have different costs. Feature weighting and instance weighting are two most common ways to tackle this problem. However, none of the current weighting methods take into account the salience of features. In order to accomplish this, a novel and flexible weighting function is proposed that dynamically assigns a proper weight to each feature. Experiments results show that the proposed weighting function is superior to current methods.