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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.11 No.4
- 발행연도
- 2017.12
- 수록면
- 130 - 141 (12page)
- DOI
- 10.5626/JCSE.2017.11.4.130
이용수
초록· 키워드
오류제보하기
Feature selection is one of the most challenging problems of pattern recognition and data mining. In this paper, a feature selection algorithm based on an improved version of binary differential evolution is proposed. The method simultaneously optimizes two feature selection criteria, namely, set approximation accuracy of rough set theory and relational algebra based derived score, in order to select the most relevant feature subset from an entire feature set. Superiority of the proposed method over other state-of-the-art methods is confirmed by experimental results, which is conducted over seven publicly available benchmark datasets of different characteristics such as a low number of objects with a high number of features, and a high number of objects with a low number of features.
목차
Abstract
I. INTRODUCTION
II. THE DIFFERENTIAL EVOLUTIONPRELIMINARIES
III. DE BASED FEATURE SELECTION
IV. EXPERIMENTAL RESULTS
V. CONCLUSIONS
REFERENCES
참고문헌 (51)
참고문헌 신청
M. Dash and H. Liu, “Feature selection for classification,” Intelligent data analysis, vol. 1, no. 3, pp. 131-156, 1997.
S. Das, “Filters, wrappers and a boosting-based hybrid for feature selection,” in Proceedings of the 18th International Conference on Machine Learning (ICML), Williamstown, MA, 2001, pp. 74-81.
Y. Saeys, I. Inza, and P. Larranaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007.
R. Bellman, “Dynamic programming and Lagrange multipliers,” Proceedings of the National Academy of Sciences of the United States of America, vol. 42, no. 10, 767, 1956.
R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1, pp. 273-324, 1997.
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