Linguistics

Philosophy

Selection (genetic algorithm)

Computer science

Thermodynamics

Physics

Pattern recognition (psychology)

Mathematics

Differential (mechanical device)

Feature selection

Feature (linguistics)

Artificial intelligence

Differential evolution

Binary number

Machine learning

Arithmetic

Information Sciences

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.

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