Gaussian-Based SMOTE Algorithm for Solving Skewed Class Distributions
Hansoo Lee, Jonggeun Kim, Sungshin Kim · International Journal of Fuzzy Logic and Intelligent Systems · 2017
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.