A novel binary many-objective feature selection algorithm for multi-label data classification

For Multi-label classification, redundant and irrelevant features degrade the performance of classification. To select the best features based on several conflicting objectives, feature selection can be modeled as a large-scale optimization problem. However, most existing multi-objective feature selection methods select the features based on minimizing two well-known objectives, the number of features and classification error, additional objectives can be considered to improve the classification performance. In this study, for the first time, a many-objective optimization method is proposed to select the efficient features for multi-label classification based on not only two mentioned objectives, but also maximizing the correlation between features and labels and minimizing the computational complexity of features. Maximizing the correlation could lead to increasing the accuracy of classification. On the other hand, selecting less complex features decreases the computational complexity of feature extraction phase. The most important aim of this paper is to tackle the multi-label feature selection based on the number of features, classification error, correlation between features and labels, and computational complexity of features, simultaneously. The conducted many-objective feature selection problem is solved using a proposed binary version of NSGA-III algorithm. The binary operator improves the exploration power of optimizer to search the large-scale space. In order to evaluate the proposed algorithm (called binary NSGA-III), a benchmarking experiments is conducted on eight multi-label datasets in terms of several multi-objective assessment metrics, including Hypervolume indicator, Pure Diversity, and Set-coverage. Experimental results show significant improvements for proposed method in comparison with other algorithms.

Automated summary

This study proposes a multi-objective optimization method to select efficient features for multi-label classification, aiming to improve classification performance by maximizing feature-label correlation and minimizing computational complexity. Experimental results show significant improvements for the proposed method on multi-label datasets compared to other algorithms.