基于标记相关性的多标记专属特征学习算法OACSTPCD

Bilabel-specific features for multi-label classification algorithm(BILAS)is a representative multi-label learning algorithm.However,it only considers samples with different values for the label pair,and ignores samples with the same value,so that the gen-erated label-specific features could not comprehensively and accurately characterize the label information.To weaken this shortcom-ing,based on the second-order correlation of labels,label-specific features are generated for all types of samples of the label pair,and a multi-label specific features learning algorithm based on label correlation is proposed.Firstly,the distance-based prototype learning method is used to select prototypes of all label pairs,and then the corresponding label-specific features are generated;fur-thermore,using the idea of label powerset,a multi-label classifier is constructed.Experimental results on five publicly available test datasets from MULAN(a Java library for multi-label learning)show that the proposed algorithm,compared to BILAS and multila-bel classification algorithm via calibrated label ranking(CLR),ranks first in terms of the comprehensive average ranking on the five multi-label evaluation metrics.Furthermore,it achieves improvements of 20.4%and 37.1%compared to BILAS and CLR,respec-tively,demonstrating the effectiveness of the proposed algorithm.