Gaussian-based SMOTE algorithm for solving skewed class distributions

Abstract

Sufficient amount of learning data is an essential condition to implement a classifier with excellent performance. However, the obtained data usually follow a significantly biased distribution of classes. It is called a class imbalance problem, which is one of the frequently occurred issues in the real world applications. This problem causes a considerable performance drop because most of the machine learning methods assume given data follow a balanced distribution of classes. The implemented classifier will derive false classification results if the problem is not solved. Therefore, this paper proposes a novel method, named as Gaussianbased SMOTE, to solve the problem by combining Gaussian distribution in a synthetic data generation process. It is confirmed that the proposed method could solve the class imbalance problem by conducting experiments with actual cases.