FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief
This work provides an incremental improvement for researchers and practitioners working with TSK fuzzy regression models, especially in high-dimensional settings.
This paper introduces FCM-RDpA, an improved method for optimizing Takagi-Sugeno-Kang (TSK) fuzzy systems for regression. It replaces grid partitioning with fuzzy c-means clustering for rule initialization and AdaBound with Powerball AdaBelief for parameter optimization, demonstrating superior performance on 22 regression datasets, particularly with high-dimensional features.
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.