Optimization approach for feature selection in multi-label classification

Nowadays, many data sources that include multi-label learning and multi-label classification have emerged in recent application areas. To achieve high classification accuracy, the multi-label feature selection method has received much attention because its accuracy can be significantly improved by selecting important features. In previous multi-label feature selection studies, a score function was designed based on the measure of the dependency between features and labels. However, identifying the optimal feature subset is an impractical task because all possible feature subsets are 2N, where N is the number of total features in a given dataset. Thus, the conventional methods utilized a greedy search approach that can be stuck in local optima. To circumvent the drawback of the greedy approaches, we design a score function based on mutual information and present a numerical optimization approach to avoid being stuck in the local optima. The experimental results demonstrate the superiority of the proposed multi-label feature selection method.