Multi-Layer Ensemble Evolving Fuzzy Inference System

Xiaowei Gu

Research output: Contribution to journalArticlepeer-review

16 Citations (SciVal)
44 Downloads (Pure)

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.

Original languageEnglish
Article number9072662
Pages (from-to)2425-2431
Number of pages7
JournalIEEE Transactions on Fuzzy Systems
Volume29
Issue number8
Early online date20 Apr 2020
DOIs
Publication statusPublished - 01 Aug 2021
Externally publishedYes

Keywords

  • Ensemble model
  • Evolving fuzzy system
  • Multilayered structure
  • Transparency

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