Multi-label feature selection method based on redundancy minimization

Feature selection methods are known to be effective in improving the learning process. The purpose of a feature selection method is to identify relevant features and remove irrelevant features in order to obtain a suitable subset of features, so that the redundancy between the selected features is minimized. In multi-label data, if there is a correlation between features, it is possible that the amount of redundancy in the feature set is increased. The existence of redundancy between features along with the challenge of high dimensions of multi-label data can grow the computational calculations, decrease the accuracy and finally increase the probability of errors in the prediction and classification of multi-label data. In this article, with the aim of minimizing the redundancy of features, a multi-label feature selection algorithm is proposed considering the least squares regression model and sparse regularization. Finally, using a number of well-known multi-label data sets, the efficiency of the proposed method is verified and the results are compared with some common multi-label feature selection methods.