A feature selection algorithm based on fuzzy integral in multi-label learning

Multi-label learning algorithms face many challenges due to the high volume and dimensions of multi-label data and the existence of noise. Feature selection methods are an effective technique for addressing these challenges. This paper presents a feature selection method based on an ensemble approach for multi-label data. In this approach, three different decision matrices based on various feature evaluation criteria, taking into account the relevancy of features with class labels and their redundancy relative to each other, are effective in the feature selection process. These three decision matrices are finally combined based on an ensemble approach using the concept of fuzzy integral to evaluate the features according to the aggregate value. Comparisons have been made with several similar algorithms to illustrate the performance of the proposed method.