Projection based fuzzy least squares twin support vector machine for class imbalance problems

TL;DR

This paper introduces two novel fuzzy least squares twin support vector machine models designed to effectively handle class imbalance and noise in datasets, demonstrating superior performance on benchmark and synthetic data.

Contribution

The paper proposes two new fuzzy SVM-based models, IF-RELSTSVM and F-RELSTSVM, incorporating hyperplane-based fuzzy memberships to improve classification in imbalanced and noisy datasets.

Findings

Outperform baseline algorithms on benchmark datasets

Effective handling of noisy and imbalanced data demonstrated

Statistical tests confirm significance of results

Abstract

Class imbalance is a major problem in many real world classification tasks. Due to the imbalance in the number of samples, the support vector machine (SVM) classifier gets biased toward the majority class. Furthermore, these samples are often observed with a certain degree of noise. Therefore, to remove these problems we propose a novel fuzzy based approach to deal with class imbalanced as well noisy datasets. We propose two approaches to address these problems. The first approach is based on the intuitionistic fuzzy membership, termed as robust energy-based intuitionistic fuzzy least squares twin support vector machine (IF-RELSTSVM). Furthermore, we introduce the concept of hyperplane-based fuzzy membership in our second approach, where the final classifier is termed as robust energy-based fuzzy least square twin support vector machine (F-RELSTSVM). By using this technique, the membership values are based on a projection based approach, where the data points are projected on the hyperplanes. The performance of the proposed algorithms is evaluated on several benchmark and synthetic datasets. The experimental results show that the proposed IF-RELSTSVM and F-RELSTSVM models outperform the baseline algorithms. Statistical tests are performed to check the significance of the proposed algorithms. The results show the applicability of the proposed algorithms on noisy as well as imbalanced datasets.