Abstract
Forming an efficient feature space for classification problems is a grand challenge in pattern recognition. Many optimization algorithms are adopted to do feature selection, but these algorithms do searching in multi-dimensions space and always cannot get the optimal feature subset. In this paper, a feature selection method with differential evolution algorithm doing searching in only one dimension real valued space is proposed to improve the classification performance. Experimental results show that the proposed method can do feature selection more effectively than the compared method and get much higher classification accuracy.
This work was supported by the Natural Science Foundation of Guangdong Province of China (No.9451503101003263), Educational Commission of Guangdong Province of China and the Youth Foundation of Shantou University.
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© 2011 Springer-Verlag Berlin Heidelberg
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Wang, J., Zhao, Y. (2011). Differential Evolution Algorithm Based One Dimension Real Valued Searching for Feature Selection. In: Zeng, D. (eds) Future Intelligent Information Systems. Lecture Notes in Electrical Engineering, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19706-2_17
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DOI: https://doi.org/10.1007/978-3-642-19706-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19705-5
Online ISBN: 978-3-642-19706-2
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