Abstract The method for learning label-specific features reduces dimensions by selecting specific features for each label with the consideration of pairwise label correlations and it solves the problem of dimensions of multi-label classification effectively. However, instance correlations are not taken into account in the method. To solve this problem, a multi-label classification algorithm based on label-specific features and instance correlations is proposed. Both label correlations and the correlation of instance features are considered. The similarity map is constructed to learn the similarity of instance feature space. Experimental results on 8 datasets show that the proposed algorithm effectively extracts label-specific features with better classification performance.
Fund:Supported by National Natural Science Foundation of China(No.61772252), Natural Science Foundation of Liaoning Province(No.2019-MS-216), Program for Liaoning Innovative Talents in University(No.LR2017044)
About author:: (ZHANG Yong(Corresponding author), Ph.D., professor. His research interests include data mining and intelligent computing.);(LIU Haoke, master student. His research interests include machine learning and intelligent computing.);(ZHANG Jie, master student. Her research interests include data mining and pattern re-cognition.)
ZHANG Yong,LIU Haoke,ZHANG Jie. Multi-label Classification Algorithm Based on Label-Specific Features and Instance Correlations[J]. , 2020, 33(5): 439-448.
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2020, Vol. 33 
