Instance-based entropy fuzzy support vector machine for imbalanced data

TL;DR

This paper introduces IEFSVM, an improved fuzzy SVM method that uses instance-based entropy of neighbors to better handle class imbalance, showing superior performance on various datasets.

Contribution

The paper proposes IEFSVM, which considers entropy diversity of neighbors for each sample, enhancing imbalance classification beyond existing fuzzy SVMs.

Findings

IEFSVM achieves higher AUC on imbalanced datasets.

It outperforms traditional SVMs and other machine learning methods.

Effective in datasets with high imbalance ratios.

Abstract

Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample's neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.