Ensemble Learning-Based Fuzzy Aggregation Functions and Their Application in TSK Neural Networks

Abstract

AbstractTakagi–Sugeno–Kang fuzzy neural networks (TSKFNN) are powerful tools to model vague or imprecise information. Regression is one of the most important tasks commonly tackled by supervised learning techniques. TSKFNNs are considered suitable models to deal with regression problems on account of their simplicity and flexibility. Aggregation methods play an important role in combining various fuzzy rules from a TSKFNN rule base to obtain a model prediction. However, many current aggregation methods rely on expert experience and human knowledge, which may be hard to acquire and could bring human bias. This paper proposes data-driven aggregation functions for rules aggregation based on ensemble learning, namely AdaBoost and bagging, which can achieve superior generalizability in testing compared with the existing rule aggregation methods. Furthermore, they can also provide insights into the importance of each rule in the model’s decision making, thus, helping to improve the interpretability of the model. Extensive experiments on 11 commonly used benchmark datasets with various sizes and dimensionalities validated the superiority of the proposed ensemble learning-based fuzzy aggregation functions compared with existing state-of-the-art TSKFNNs.