Abstract
Semi-supervised learning from data streams is widely considered as a highly challenging task to be further researched. In this paper, a novel dual-model self-organizing fuzzy inference system composed of two recently introduced evolving fuzzy systems (EFSs) is proposed for semi-supervised learning from data streams in infinite delay environments. After being primed with a small amount of labelled data during the warm-up period, the proposed model is able to continuously self-learn and self-expand its knowledge base from unlabelled data on a chunk-by-chunk basis with minimal human expert involvement. Thanks to its dual-model structure, the proposed model combines the merits of the two EFS models such that it can continuously identify new prototypes from new pseudo-labelled data to self-improve its knowledge base whilst keeping the impact of pseudo-labelled errors on its decision-making minimized. Numerical examples based on various benchmark problems demonstrate the efficacy of the proposed method, showing its strong potential in real-world applications by offering higher classification accuracy over the state-of-the-art competitors whilst retaining high computational efficiency.
•A dual-model semi-supervised system is proposed for data stream classification in infinite label delay scenarios.•A unique semi-supervised learning strategy is designed for its dual-model structure.•The system continuously self-improves its knowledge base from unlabelled streaming data via pseudo-labelling.