A novel approach for learning label correlation with application to feature selection of multi-label data

Abstract

Each example of multi-label data is represented in an object with its feature vector (i.e. an instance) while being related to multiple labels. Learning label correlation can effectively reduce the labels needed to be predicted and optimize the classification performance. For this reason, label correlation plays a crucial role in multi-label learning and has been explored by many existing algorithms. Generally, every label has its own indispensable features, and it is reasonable to assume that a higher repetitiveness of indispensable features represents higher correlations among labels. Inspired by this fact, the essential elements for each label, which are composed of indispensable features, are constructed in this paper. A method is proposed for learning label correlation and applying it to feature selection of multi-label data based on the overlap of different families of essential elements related to label. Firstly, the essential elements of each label are defined and characterized to reflect the internal connection between features and label. In addition, a process for calculating the essential elements of a single label is provided. Secondly, by considering the overlap of the essential element collections that are determined by the different labels, relevancy of label and corresponding relevance judgement matrix with the label set are described. Therefore, several labels with strong relationships are assigned to a relevant group of labels. Meanwhile, local and global label correlations can be computed. Thus a novel multi-label learning algorithm, called CLSF, is presented to select a compact subset of indispensable features for each relevant group of labels by applying local label correlation to feature selection of multi-label data. Finally, comprehensive experiments on eleven benchmark data sets clearly illustrate the effectiveness and efficiency of CLSF against five other multi-label learning algorithms.