A Self-Constructing Multi-Expert Fuzzy System for High-dimensional Data Classification

TL;DR

This paper introduces the Self-Constructing Multi-Expert Fuzzy System (SOME-FS), a novel approach designed to improve high-dimensional data classification by addressing challenges like noise, vanishing gradients, and rule explosion.

Contribution

It presents a new fuzzy system that combines mixed structure learning and multi-expert strategies, enabling effective structure determination and robustness without prior knowledge.

Findings

Effective in high-dimensional tabular data

Handles uncertainty well

Identifies concise core rules

Abstract

Fuzzy Neural Networks (FNNs) are effective machine learning models for classification tasks, commonly based on the Takagi-Sugeno-Kang (TSK) fuzzy system. However, when faced with high-dimensional data, especially with noise, FNNs encounter challenges such as vanishing gradients, excessive fuzzy rules, and limited access to prior knowledge. To address these challenges, we propose a novel fuzzy system, the Self-Constructing Multi-Expert Fuzzy System (SOME-FS). It combines two learning strategies: mixed structure learning and multi-expert advanced learning. The former enables each base classifier to effectively determine its structure without requiring prior knowledge, while the latter tackles the issue of vanishing gradients by enabling each rule to focus on its local region, thereby enhancing the robustness of the fuzzy classifiers. The overall ensemble architecture enhances the stability and prediction performance of the fuzzy system. Our experimental results demonstrate that the proposed SOME-FS is effective in high-dimensional tabular data, especially in dealing with uncertainty. Moreover, our stable rule mining process can identify concise and core rules learned by the SOME-FS.