Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

TL;DR

This paper introduces a novel concise fuzzy system modeling approach that combines enhanced soft subspace clustering and sparse learning to improve interpretability and reduce rule complexity in high-dimensional data modeling.

Contribution

It proposes a new method integrating ESSC and sparse learning for constructing concise zero-order TSK fuzzy systems, enhancing interpretability and efficiency.

Findings

Effective reduction in fuzzy rule numbers.

Improved interpretability in high-dimensional data.

Validated on real-world datasets with positive results.

Abstract

The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy rules for high-dimensional data and lead to degeneration in interpretability. Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS. To address these issues, a concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of enhanced soft subspace clustering (ESSC) and sparse learning (SL). In this method, ESSC is used to generate the antecedents and various sparse subspace for different fuzzy rules, whereas SL is used to optimize the consequent parameters of the fuzzy rules, based on which the number of fuzzy rules can be effectively reduced. Finally, the proposed ESSC-SL-CTSK-FS method is used to construct con-cise zero-order TSK FS that can explain the scenes in high-dimensional data modeling more clearly and easily. Experiments are conducted on various real-world datasets to confirm the advantages.