An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification.

• A novel semi-supervised self-organizing fuzzy inference system (S3OFIS+) is introduced for data stream classification. • A novel parameter-free pseudo-labelling strategy is proposed for S3OFIS+ to effectively self-train from unlabelled data. • The proposed S3OFIS+ can self-learn from unlabelled data streams chunk-by-chunk in a single-pass manner with minimum human input. • The proposed S3OFIS+ can provide users high interpretability and explainability thanks to its prototype-based nature. 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. [ABSTRACT FROM AUTHOR]