FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief

TL;DR

This paper introduces FCM-RDpA, an improved TSK fuzzy regression model that enhances rule initialization and optimization techniques, demonstrating superior performance on diverse datasets especially with high-dimensional features.

Contribution

The paper proposes FCM-RDpA, replacing grid partition with fuzzy c-means and integrating Powerball AdaBelief for better optimization, outperforming previous methods.

Findings

Outperforms MBGD-RDA on 22 datasets.

Particularly effective with high-dimensional data.

Fuzzy c-means improves rule initialization.

Abstract

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules.