A label-specific multi-label feature selection algorithm based on the Pareto dominance concept.

Highlights • Unlike common multi-label feature selection, the proposed method derives from different cognitive standpoint. • Feature selection process is directly done on multi-label data, and there is no need to data transformation. • The proposed method tries to find label-specific features which are the most discriminative features for each label. • Also, an extension of our method is presented which selects a pre-defined number of features. • The proposed method is appropriate to both numerical and nominal features. • The proposed method is effective and fast. Abstract In multi-label data, each instance is associated with a set of labels, instead of one label. Similar to single-label data, feature selection plays an important role in improving classification performance. In multi-label classification, each class label might be specified by some particular characteristics of its own which are called label-specific features. In this paper, a fast accurate filter-based feature selection method is exclusively designed for multi-label datasets to find label-specific features. It maps the features to a multi-dimensional space based on a filter method, and selects the most salient features with the help of Pareto-dominance concepts from multi-objective optimization domain. Our proposed method can be used as online feature selection that deals with problems in which features arrive sequentially while the number of data samples is fixed. In this method, the number of features to be selected is specified during the process of feature selection. However, sometimes it is desired to predefine the number of features. For this reason, an extension of the proposed method is presented to solve this problem. To prove the performance of the proposed methods, several experiments are conducted on some multi-label datasets and the results are compared to five well-established multi-label feature selection methods. The results show the superiority of the proposed methods in terms of different multi-label classification criteria and execution time. [ABSTRACT FROM AUTHOR]