1. School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China; 2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, Shanxi 030006,China
Abstract：Multi-label learning is proposed to deal with the ambiguity problem in which a single sample is associated with multiple concept labels simultaneously, while the semi-supervised multi-label learning is a new research direction in recent years. To further exploit the information of unlabeled samples, a semi-supervised multi-label learning algorithm based on Tri-training(MKSMLT) is proposed. It adopts ML-kNN algorithm to get more labeled samples, then employs the Tri-training algorithm to use three classifiers to rank the unlabeled samples. Experimental results illustrate that the proposed algorithm can effectively improve the classification performance.
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2015, Vol. 29 
