A Novel Multi-objective Binary Differential Evolution Algorithm for Multi-label Feature Selection

Abstract

In machine learning, multi-label classification aims to assign labels of instances in a dataset which are associated to more than one class label. Feature selection as an important task in predictive model construction improves the performance of multi-label classification. Since feature selection task can be interpreted as optimizing multiple objectives in a massive search space, multi-objective evolutionary techniques can be applied to tackle this family of problems. In this paper, a binary multi-objective feature selection is proposed for multi-label data with considering number of features and classification accuracy as objectives. A binary differential evolution is proposed based on opposition-based learning concept and partially voting between two candidate solutions to decide about absence or presence of a feature in third randomly selected solution. Because feature selection is basically a binary optimization problem, proposing a binary operator improves the effectiveness of search process in evolutionary algorithms. The proposed operator is utilized in third version of Generalized Differential Evolution (GDE3) which is a multi-objective optimization algorithm to select best subset of multi-label features with minimum number of features. A benchmarking is conducted on eight multi-label datasets in terms of several multi-objective assessment metrics including the Hypervolume indicator, Pure Diversity, and Set-coverage. Experimental results show significant improvements for proposed method in comparison with the state-of-the-art multi-objective feature selection methods for multi-label classification, which are namely NSGA-II and PSO based approaches.