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arXiv · 2501.12607

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

Qiong Liu·Mingjie Cai·Qingguo Li
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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.

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Qiong Liu, Mingjie Cai, Qingguo Li. 2025-01-22. Low-Dimensional Representation-Driven TSK Fuzzy System for Feature Selection. https://doi.org/10.1109/tfuzz.2025.3608935

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