FLEX-SMOTE: Synthetic over-sampling technique that flexibly adjusts to different minority class distributions
Chumphol Bunkhumpornpat, Ekkarat Boonchieng, Varin Chouvatut, David B. Lipsky
Abstract
Class imbalance is a challenge that affects the prediction rate on a minority class. To remedy this problem, various SMOTEs (synthetic minority over-sampling techniques) have been designed to populate synthetic minority instances. Some SMOTEs operate on the border of a minority class, while others concentrate on the class core. Unfortunately, it is difficult to put the right SMOTE to the right dataset because distributions of classes are varied and might not be obvious. This paper proposes a new technique, called FLEX-SMOTE, that is flexible enough to be used with all sorts of datasets. The key idea is that an over-sampled region is selected based on the characteristics of minority classes. This approach is based on a density function that is used to describe the distributions of minority classes. Herein, we have included experimental results showing that FLEX-SMOTE can significantly improve the predictive performance of a minority class.