Semi-supervised multiview fuzzy broad learning

Semi-supervised learning models often rely on restricted assumptions, and can easily suffer from covariate shift or noise. Few studies have investigated the use of fuzzy rule-based methods in the semi-supervised discipline. To improve model accuracy against covariate shift and to introduce fuzzy methods for interpretability, we first build a semi-supervised fuzzy broad learning model named SSFBLS, which employs a Mean-Teacher framework. Then, a trusted multiview semi-supervised classification method, termed TMSSC, is proposed by integrating the SSFBLS with a multiview fusion network to enhance the robustness of the model. Under the Mean-Teacher framework, SSFBLS involves the Takagi-Sugeno-Kang fuzzy model which can effectively deal with imprecision and uncertainty, and broad learning system which has strong learning ability and high computational efficiency. TMSSC utilizes a trusted mechanism to blend multiple views, so as to enhance its learning ability in semi-supervised scenarios. Experiments on the benchmark datasets demonstrate that the proposed methods have better anti-noise ability, competitive classification accuracy, as well as fast running speed. •We build a fuzzy broad semi-supervised learning model which integrates the Mean-Teacher model, fuzzy board learning into a unified framework.•We suggest an end-to-end multiview semi-supervised classification method that merges the fuzzy broad model and a multiview fusion network.•The proposed methods can handle large scale datasets with varied distributions and show competitive learning ability with fewer labeled data.•The proposed method has certain degree of interpretability while providing an appreciable classification performance.