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Differential evolution (DE) for multi-objective feature selection in classification by Bing Xue; Wenlong Fu; Mengjie Zhang is a scholarly article available to read on EtoBox.

What is Differential evolution (DE) for multi-objective feature selection in classification about?

Feature selection has two main conflicting objectives, which are to minimise the number of features and maximise the classification accuracy. Evolutionary computation techniques are particularly suitable for solving mult-objective tasks. Based on differential evolution (DE), this paper develops a multi-objective feature selection algorithm (DEMOFS). DEMOFS is examined and compared with two traditional feature selection algorithms and a DE based single objective feature selection algorithm. DEFS aims to minimise the classification error rate of the selected features. Experiments on nine benchmark datasets show that in almost all cases, DEMOFS outperforms DEFS and the two traditional feature selection methods in terms of both the number of features and the classification performance.

Author

Bing Xue; Wenlong Fu; Mengjie Zhang

Publisher

ACM

Published

2014

Language

EN