Multilayer Ensemble Evolving Fuzzy Inference System

Abstract

In order to tackle high dimensional, complex problems, learning models have to go deeper. In this article, a novel multilayer ensemble learning model with first-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on a sample-by-sample basis and self-organizes its multilayered system structure and meta-parameters in a feedforward, noniterative manner. Benefiting from its multilayered distributed representation learning ability, the ensemble system not only demonstrates the state-of-the-art performance on various problems, but also offers high level of system transparency and explainability. Theoretical justifications and experimental investigation show the validity and effectiveness of the proposed concept and general principles.