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Computer and Computing Technologies in Agriculture IX 9th IFIP WG 5.14 International Conference, CCTA 2015 Beijing, China, September 27–30, 2015, Part I |
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Conference papers
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.
Cited literature [16 references]
https://hal.inria.fr/hal-01557791
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 3:49:33 PM
Last modification on : Thursday, July 6, 2017 - 3:54:54 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 9:02:17 PM
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 3:49:33 PM
Last modification on : Thursday, July 6, 2017 - 3:54:54 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 9:02:17 PM
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