Self-organizing fuzzy inference ensemble system for big streaming data classification

Gu, X. and Angelov, P. and Zhao, Z. (2021) Self-organizing fuzzy inference ensemble system for big streaming data classification. Knowledge-Based Systems, 218. ISSN 0950-7051

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Abstract

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.

Item Type:
Journal Article
Journal or Publication Title:
Knowledge-Based Systems
Additional Information:
This is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems, 218, 2021 DOI: 10.1016/j.knosys.2021.106870
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1802
Subjects:
ID Code:
153033
Deposited By:
Deposited On:
22 Mar 2021 14:25
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jun 2021 09:05