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Comparative Study on Metaheuristic-Based Feature Selection for Cotton Foreign Fibers Recognition

Abstract : The excellent feature set or feature combination of cotton foreign fibers is great significant to improve the performance of machine-vision-based recognition system of cotton foreign fibers. To find the excellent feature sets of foreign fibers, in this paper presents three metaheuristic-based feature selection approaches for cotton foreign fibers recognition, which are particle swarm optimization, ant colony optimization and genetic algorithm, respectively. The k-nearest neighbor classifier and support vector machine classifier with k-fold cross validation are used to evaluate the quality of feature subset and identify the cotton foreign fibers. The results show that the metaheuristic-based feature selection methods can efficiently find the optimal feature sets consisting of a few features. It is highly significant to improve the performance of recognition system for cotton foreign fibers.
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Xuehua Zhao, Xueyan Liu, Daoliang Li, Huiling Chen, Shuangyin Liu, et al.. Comparative Study on Metaheuristic-Based Feature Selection for Cotton Foreign Fibers Recognition. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. pp.8-18, ⟨1010.1007/978-3-319-48357-3_2⟩. ⟨hal-01557791⟩

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