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Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Tinghuai Ma1,*, Honghao Zhou1, Dongdong Jia1, Abdullah Al-Dhelaan2, Mohammed Al-Dhelaan2, Yuan Tian3
1 College of Computer and Software, Nanjing University of information science & Technology, Nanjing, China.
2 College of Computer and Information Sciences, KingSaud University, Riyadh, Saudi Arabia.
3 Nanjing Institute of Technology, Nanjing, China.
∗ Corresponding Author: Tinghuai Ma. Email: .

Computer Modeling in Engineering & Sciences 2019, 121(2), 569-592.


Feature selection has been widely used in data mining and machine learning. Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly. In this article, a feature selection algorithm with local search strategy based on the forest optimization algorithm, namely FSLSFOA, is proposed. The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest. Next, the fitness function is improved, which not only considers the classification accuracy, but also considers the size of the feature subset. To avoid falling into local optimum, a novel global seeding method is attempted, which selects trees on the bottom of candidate set and gives the algorithm more diversities. Finally, FSLSFOA is compared with four feature selection methods to verify its effectiveness. Most of the results are superior to these comparative methods.


Feature selection, local search strategy, forest optimization, fitness function.

Cite This Article

Ma, T., Zhou, H., Jia, D., Al-Dhelaan, A., Al-Dhelaan, M. et al. (2019). Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm. CMES-Computer Modeling in Engineering & Sciences, 121(2), 569–592.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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