Binary differential evolution with self-learning for multi-objective feature selection

• Published in: Information sciences Vol. 507; pp. 67 - 85
• Main Authors: Zhang, Yong, Gong, Dun-wei, Gao, Xiao-zhi, Tian, Tian, Sun, Xiao-yan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2020
• Subjects: Differential evolution; Feature selection; Multi-objective optimization; Self-learning; Differential evolution; Feature selection; Multi-objective optimization; Self-learning
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2019.08.040

•Proposing a binary differential evolution algorithm with self-learning strategy, called MOFS-BDE, to solve multi-objective feature selection problems.•Proposing a new binary mutation operator based on probability difference to guide the individuals to locate potentially optimal areas fast.•Proposing a new one-bit purifying search operator (OPS) for improving the self-learning capability of elite individuals.•Proposing an efficient non-dominated sorting operator with crowding distance to reduce the time consumption of the selection operator in differential evolution. Feature selection is an important data preprocessing method. This paper studies a new multi-objective feature selection approach, called the Binary Differential Evolution with self-learning (MOFS-BDE). Three new operators are proposed and embedded into the MOFS-BDE to improve its performance. The novel binary mutation operator based on probability difference can guide individuals to rapidly locate potentially optimal areas, the developed One-bit Purifying Search operator (OPS) can improve the self-learning capability of the elite individuals located in the optimal areas, and the efficient non-dominated sorting operator with crowding distance can reduce the computational complexity of the selection operator in the differential evolution. Experimental results on a series of public datasets show that the effective combination of the binary mutation and OPS makes our MOFS-BDE achieve a trade-off between local exploitation and global exploration. The proposed method is competitive in comparison with some representative genetic algorithm-, particle swarm-, differential evolution-, and artificial bee colony-based feature selection algorithms. [Display omitted]