Multi-Label Takagi-Sugeno-Kang Fuzzy System

TL;DR

This paper introduces ML-TSK FS, a fuzzy system for multi-label classification that models feature-label relationships with fuzzy rules, demonstrating competitive performance on benchmark datasets.

Contribution

The paper presents a novel multi-label fuzzy system that integrates fuzzy inference and regression loss to improve classification accuracy.

Findings

ML-TSK FS performs competitively on 12 benchmark datasets.

Fuzzy rules effectively model feature-label relationships.

The method enhances classification performance compared to existing approaches.

Abstract

Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However,the modeling of the relationship between the features and the labels is critical to the classification performance. To this end, we propose a new multi-label classification method, called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The fuzzy system is trained by integrating fuzzy inference based multi-label correlation learning with multi-label regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets. 1 The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance.