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An Explainable Semi-Supervised Self-Organizing Fuzzy Inference System for Streaming Data Classification

Gu, Xiaowei (2022) An Explainable Semi-Supervised Self-Organizing Fuzzy Inference System for Streaming Data Classification. Information Sciences, 583 . pp. 364-385. ISSN 0020-0255. (doi:10.1016/j.ins.2021.11.047) (KAR id:91573)


As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from data in a supervised manner. However, high-quality labelled data can be difficult to obtain in many real-world classification applications concerning data streams, though unlabelled data is plentiful. To overcome the labelling bottleneck and construct a stronger classification model, a novel semi-supervised EIS is proposed in this paper. After being primed with a small amount of labelled data, the proposed method is capable of continuously self-developing its system structure and self-updating the meta-parameters from unlabelled data streams chunk-by-chunk in a non-iterative, exploratory manner by exploiting a novel pseudo-labelling strategy. Thanks to its transparent prototype-based structure and human-understandable reasoning process, the proposed method can provide users high explainability and interpretability while achieving great classification precision. Experimental investigation demonstrates the superior performance of the proposed method.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2021.11.047
Uncontrolled keywords: data stream classification; evolving intelligent system; semi-supervised learning; pseudo-labelling;
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Xiaowei Gu
Date Deposited: 15 Nov 2021 11:05 UTC
Last Modified: 19 Nov 2022 00:00 UTC
Resource URI: (The current URI for this page, for reference purposes)

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