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

By Yong Zhang, D. Gong, Xiao-zhi Gao, Tian Tian, Xiaoyan Sun. Information Sciences, 2020

399 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/76c0388fb4587a33ad4e49b71aae2c5e162ff810

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ayça Deniz
2. Tansel Dökeroğlu
3. Mehrdad Rostami
4. Hakan Ezgi Kiziloz
5. Saman Forouzandeh
6. Kamal Berahmand

In Computational Theory and Mathematics

1. Ayça Deniz
2. Tansel Dökeroğlu
3. Mehrdad Rostami
4. Hakan Ezgi Kiziloz
5. Saman Forouzandeh
6. Kamal Berahmand

Citations in the corpus

• Review of Swarm Intelligence-based Feature Selection Methods (2020, methodology): “In (Wang, Zhang et al. 2020), an ABC-based feature selection is proposed by integrating of multi-objective optimization algorithm with a sample reduction strategy.”
• A comprehensive survey on recent metaheuristics for feature selection (2022, background): “studied a new multiobjective binary DE with a self-learning feature selection approach [233].”