A two-stage feature selection method with its application. (October 2015)

• Record Type: Journal Article
• Authors: Zhao, Xuehua; Li, Daoliang; Yang, Bo; Chen, Huiling; Yang, Xinbin; Yu, Chenglong; Liu, Shuangyin
• Is Part Of: Computers & electrical engineering. Volume 47(2015)
• Page Start: 114
• Page End: 125
• Publication Date: 2015-10
• Subjects: Foreign fibers -- Feature selection -- Information gain -- Binary particle swarm optimization; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.08.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A two-stage selection algorithm combining IG and BPSO for foreign fiber data. IG can filter noisy features and the BPSO can select the highly discriminating features. The experiments show that our method can find the subsets with small size and high accuracy. The optimum subsets are significant for online detection based on machine vision of foreign fibers in cotton. Abstract: Foreign fibers in cotton seriously affect the quality of cotton products. Online detection systems of foreign fibers based on machine vision are the efficient tools to minimize the harmful effects of foreign fibers. The optimum feature set with small size and high accuracy can efficiently improve the performance of online detection systems. To find the optimal feature sets, a two-stage feature selection algorithm combining IG (Information Gain) approach and BPSO (Binary Particle Swarm Optimization) is proposed for foreign fiber data. In the first stage, IG approach is used to filter noisy features, and the BPSO uses the classifier accuracy as a fitness function to select the highly discriminating features in the second stage. The proposed algorithm is tested on foreign fiber dataset. The experimental results show that the proposed algorithm can efficiently find the feature subsets with smaller size and higher accuracy than other algorithms. Graphical abstract: