A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling

Abstract

Classification problems often face challenges when dealing with imbalanced datasets, leading to decreased performance. To address this issue, balancing the dataset becomes imperative for improved classification accuracy. Among various methods proposed in the literature, oversampling techniques are fundamental approaches to mitigating class imbalance. Synthetic Minority Over-sampling Technique (SMOTE) is a foundational technique in this domain. However, a more refined approach, Safe-Level-SMOTE, selectively utilizes crucial minority instances to generate synthetic samples. Another notable method, the Differential Evolution-Based Oversampling Approach for Highly Imbalanced Datasets (DEBOHID), leverages a differential evolution algorithm to handle highly imbalanced datasets effectively. This study presents a novel oversampling method (SL-D) that integrates Safe-Level-SMOTE with DEBOHID. SL-D offers three distinct variants: SL-D-Max, SL-D-Min, and SL-D-Mean, each tailored to specific scenarios. We introduce an adaptive calculation mechanism for the proposed method's crossover rate (CR) parameter. Our experimentation utilizes Decision Trees (DT), Support Vector Machines (SVM), and k-nearest neighbor (kNN) classifiers across forty-four highly imbalanced datasets. Results indicate that the SL-D-Max variant outperforms nine state-of-the-art oversampling approaches, as evidenced by superior performance metrics such as G-Mean and Area Under the Curve (AUC). Furthermore, statistical analysis employing the Friedman Test confirms the significant superiority of SL-D-Max. This study underscores the efficacy of the proposed hybrid oversampling technique in addressing imbalanced data classification challenges and highlights its potential for practical applications.