High-Dimensional Fuzzy Inference Systems

Article Type

Research Article

Publication Title

IEEE Transactions on Systems Man and Cybernetics Systems

Abstract

Fuzzy inference systems (FISs) have been developed for many years but the use of FISs for high-dimensional problems is still a challenging task. The most frequently used T-norms for computing the firing strengths are product and minimum operators of which the former is often preferred because of its differentiability. However, for high-dimensional problems, the product T-norm suffers from the numeric underflow problem. Here, we primarily focus on addressing the problem that is associated with the use of the T-norms for designing high-dimensional FISs (HDFISs). For the product T-norm, we construct an HDFIS named HDFIS-prod, which easily escapes from the numeric underflow problem. The main novelty is that we propose an adaptive dimension-dependent membership function (DMF). For the minimum T-norm, an empirical observation led us to develop a mechanism that has the natural ability to deal with super high-dimensional problems, which results in another HDFIS named HDFIS-min. Both HDFIS-prod and HDFIS-min are tested on 18 datasets with feature dimensions varying from 1024 to 120450. The simulation results demonstrate that both of them have competitive performance on handling high-dimensional datasets.

First Page

507

Last Page

519

DOI

10.1109/TSMC.2023.3311475

Publication Date

1-1-2024

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