A Multi-label Feature Selection Method Based on Multi-objectives Optimization by Ratio Analysis

Multi-label learning affected from curse of dimensionality, therefore, feature selection is vital. In this paper, we propose feature ranking method using one of the well-known Multi Attribute Decision Making (MADM) technique. This method formulates Multi-Objectives Optimization by Ratio Analysis (MOORA) for multi-label feature selection. The proposed MOORA-Based Multi-Label Feature Selection (MBMLFS) uses Feature-Feature correlation as non-beneficial criteria and Feature-Label correlation as beneficial criteria to form the decision matrix. Person’s correlation and cosine similarity is used to compute correlation of features with features and labels, respectively. The MBMLFS uses decision matrix to rank the features. Six benchmark datasets (Bibtex, Birds, Corel5k, Scence, Enron, and Medical) are used to evaluate the MBMLFS. The MBMLFS is compared with five baseline feature selection methods. Results are statistically (Wilcoxon) tested to show the significance. The experiment shows that the proposed MBMLFS outperforms. The proposed MBMLFS is taking less execution time and won 83% of the time when compared to other well-known methods.