Multi-Class Imbalanced Learning with Support Vector Machines via Differential Evolution

TL;DR

This paper introduces an improved multi-class SVM method that uses differential evolution to optimize parameters, effectively addressing class imbalance and outperforming baseline methods in classification tasks.

Contribution

The paper proposes an innovative SVM framework combined with differential evolution for multi-class imbalanced learning, integrating cost-sensitive techniques and margin adjustments.

Findings

i-SVM-DE outperforms baseline methods statistically.

The method effectively handles class imbalance.

Optimization of parameters improves classification accuracy.

Abstract

Support vector machine (SVM) is a powerful machine learning algorithm to handle classification tasks. However, the classical SVM is developed for binary problems with the assumption of balanced datasets. Obviously, the multi-class imbalanced classification problems are more complex. In this paper, we propose an improved SVM via Differential Evolution (i-SVM-DE) method to deal with it. An improved SVM (i-SVM) model is proposed to handle the data imbalance by combining cost sensitive technique and separation margin modification in the constraints, which formalize a parameter optimization problem. By using one-versus-one (OVO) scheme, a multi-class problem is decomposed into a number of binary subproblems. A large optimization problem is formalized through concatenating the parameters in the binary subproblems. To find the optimal model effectively and learn the support vectors for each class simultaneously, an improved differential evolution (DE) algorithm is applied to solve this large optimization problem. Instead of the validation set, we propose the fitness functions to evaluate the learned model and obtain the optimal parameters in the search process of DE. A series of experiments are carried out to verify the benefits of our proposed method. The results indicate that i-SVM-DE is statistically superior by comparing with the other baseline methods.