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Multi-Layer Ensemble Evolving Fuzzy Inference System

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>20/04/2020
<mark>Journal</mark>IEEE Transactions on Fuzzy Systems
Publication StatusE-pub ahead of print
Early online date20/04/20
<mark>Original language</mark>English

Abstract

In order to tackle high-dimensional, complex problems, learning models have to go deeper. In this paper, a novel multi-layer ensemble learning model with firrst-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 multi-layered system structure and meta-parameters in a feed-forward, non-iterative manner. Benefiting from its multi-layered 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.

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©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.