Semi-supervised fuzzy broad learning system based on mean-teacher model

• Published in: Pattern analysis and applications : PAA Vol. 27; no. 1
• Main Authors: Fan, Zizhu, Huang, Yijing, Xi, Chao, Peng, Cheng, Wang, Shitong
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2024; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Science; Distillation; Fuzzy logic; Labels; Machine learning; Neural networks; Pattern Recognition; Semi-supervised learning; Teachers; Theoretical Advances; Broad learning system (BLS); Semi-supervised learning; Takagi–Sugeno (TS) fuzzy system; Classification
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-024-01217-8

Fuzzy broad learning system (FBLS) is a newly proposed fuzzy system, which introduces Takagi–Sugeno fuzzy model into broad learning system. It has shown that FBLS has better nonlinear fitting ability and faster calculation speed than the most of fuzzy neural networks proposed earlier. At the same time, compared to other fuzzy neural networks, FBLS has fewer rules and lower cost of training time. However, label errors or missing are prone to appear in large-scale dataset, which will greatly reduce the performance of FBLS. Therefore, how to use limited label information to train a powerful classifier is an important challenge. In order to address this problem, we introduce Mean-Teacher model for the fuzzy broad learning system. We use the Mean-Teacher model to rebuild the weights of the output layer of FBLS, and use the Teacher–Student model to train FBLS. The proposed model is an implementation of semi-supervised learning which integrates fuzzy logic and broad learning system in the Mean-Teacher-based knowledge distillation framework. Finally, we have proved the great performance of Mean-Teacher-based fuzzy broad learning system (MT-FBLS) through a large number of experiments.