首页|An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification
An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification
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NSTL
Elsevier
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 explain ability and interpretability while achieving great classification precision. Experimental investigation demonstrates the superior performance of the proposed method. (C) 2021 Elsevier Inc. All rights reserved.
Data stream classificationEvolving intelligent systemSemi-supervised learningPseudo-labellingEVOLVING FUZZYIDENTIFICATIONCLASSIFIERSREGRESSION
NSTL
Elsevier
