LAUSR

Abstract Multi-label classification has attracted great attention from different domains. RAkEL (Random k-label sets) is an effective high-order multi-label learning approach. However, this method exploits label correlations randomly, which cannot make full use of label correlations, causing poor performance of sub-classifiers. In the paper, a novel ensemble method is proposed for multi-label classification. Instead of randomly selecting small-size subsets, this paper constructs a set of k-labelset based on local positive and negative pairwise label correlations, which are captured by KNN and matrix similarity measure. Furthermore, considering the possible missing or noisy labels in predictions, a rectification mechanism based on local label correlations is established to lessen global errors, which enhances the accuracy of the classification procedure. The experimental results over eight benchmark datasets based on multiple evaluation metrics show that these two strategies can achieve relatively better performance compared with the RAkEL method and other state-of-the-art approaches.