Multi-label Classification Algorithm Based on Label-Specific Features and Instance Correlations
张永, 刘浩科, 张洁 · 2020
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.