A review of research on co‐training

• Published in: Concurrency and computation Vol. 35; no. 18
• Main Authors: Ning, Xin, Wang, Xinran, Xu, Shaohui, Cai, Weiwei, Zhang, Liping, Yu, Lina, Li, Wenfa
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 15-08-2023
• Subjects: Algorithms; co‐training algorithm; label confidence; Machine learning; semi‐supervised learning; Supervised learning; unlabeled data
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6276

Summary
Co‐training algorithm is one of the main methods of semi‐supervised learning in machine learning, which explores the effective information in unlabeled data by multi‐learner collaboration. Based on the development of co‐training algorithm, the research work in recent years was further summarized in this article. In particular, three main steps of relevant co‐training algorithms are introduced: view acquisition, learners' differentiation, and label confidence estimation. Finally, we summarized the problems existing in the current co‐training methods, gave some suggestions for improvement, and looked forward to the future development direction of the co‐training algorithm.