Low-Dimensional Representation-Driven TSK Fuzzy System for Feature Selection

TL;DR

This paper introduces a novel feature selection method combining subspace learning and TSK fuzzy systems, effectively reducing dimensionality while maintaining information and improving discrimination for better feature selection.

Contribution

It proposes an integrated approach that uses a projection matrix and TSK-FS with a slack firing strength and $,1$-norm for enhanced feature selection, addressing limitations of existing methods.

Findings

Outperforms six state-of-the-art methods on eighteen datasets.

Effectively reduces information loss during feature selection.

Demonstrates superior discrimination capability in feature selection.

Abstract

Feature selection can select important features to address dimensional curses. Subspace learning, a widely used dimensionality reduction method, can project the original data into a low-dimensional space. However, the low-dimensional representation is often transformed back into the original space, resulting in information loss. Additionally, gate function-based methods in Takagi-Sugeno-Kang fuzzy system (TSK-FS) are commonly less discrimination. To address these issues, this paper proposes a novel feature selection method that integrates subspace learning with TSK-FS. Specifically, a projection matrix is used to fit the intrinsic low-dimensional representation. Subsequently, the low-dimensional representation is fed to TSK-FS to measure its availability. The firing strength is slacked so that TSK-FS is not limited by numerical underflow. Finally, the $\ell _{2,1}$-norm is introduced to select significant features and the connection to related works is discussed. The proposed method is evaluated against six state-of-the-art methods on eighteen datasets, and the results demonstrate the superiority of the proposed method.