提出自构建多专家模糊系统，提升高维数据分类稳定性与准确性。
A Self-Constructing Multi-Expert Fuzzy System for High-dimensional Data Classification
- 通过自构建结构学习，无需先验知识自动确定分类器结构
- 多专家机制缓解梯度消失，提升对噪声数据的鲁棒性
- 适合处理高维表格数据中的不确定性问题，规则简洁可解释
模糊神经网络（FNN）在分类任务中表现良好，通常基于Takagi-Sugeno-Kang（TSK）模糊系统。然而面对高维数据，尤其是含噪声的数据时，传统FNN常出现梯度消失、规则过多以及缺乏先验知识等问题。为此，本文提出一种新型模糊系统——自构建多专家模糊系统（SOME-FS），融合混合结构学习与多专家进阶学习两种策略。前者使每个基础分类器能自主确定结构，无需依赖先验知识；后者通过让每条规则聚焦局部区域，缓解梯度消失问题，增强分类器鲁棒性。整体集成架构提升了模糊系统的稳定性与预测性能。实验表明，SOME-FS在高维表格数据上表现优异，尤其擅长处理不确定性。此外，其稳定的规则挖掘过程可提取出简洁且核心的学习规则。
原文摘要 · Abstract (English)
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.
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