Abstract
Feature selection are highly important to improve the classification accuracy of recognition systems for foreign matter in cotton. To address this problem, this paper presents six filter approaches of feature selection for obtaining the good feature combination with high classification accuracy and small size, and make comparisons using support vector machine and k-nearest neighbor classifier. The result shows that filter approach can efficiently find the good feature sets with high classification accuracy and small size, and the selected feature sets can effectively improve the performance of recognition system for foreign matter in cotton. The selected feature combination has smaller size and higher accuracy than original feature combination. It is important for developing the recognition systems for cotton matter using machine vision technology.
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Acknowledgments
This research is supported by MOE (Ministry of Education in China) Youth Fund Project of Humanities and Social Sciences (17YJCZH261), Guangdong Natural Science Foundation (2018A030313339), Guangdong Universities Characteristic Innovation Projects (2017GKTSCX063), Science Research Cultivation Project of Shenzhen Institute of Information Technology (ZY201718), Guangdong College Students Cultivation of Scientific, Technological Innovation Special Funds (pdjh2018b0861) and Shenzhen 13th Five-Year Plan Project of Philosophy and Social Sciences (SZ2018D017).
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Zhao, X. et al. (2019). Feature Selection for Cotton Matter Classification. In: Li, D. (eds) Computer and Computing Technologies in Agriculture X. CCTA 2016. IFIP Advances in Information and Communication Technology, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-030-06155-5_48
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DOI: https://doi.org/10.1007/978-3-030-06155-5_48
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