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논문 기본 정보

자료유형
학술저널
저자정보
(Neotia Institute of Technology, Management and Science) (Chung-Shan Medical University) (Indian Institute of Engineering Science and Technology) (Indian Institute of Engineering Science and Technology)
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
수록면
130 - 141 (12page)
DOI
10.5626/JCSE.2017.11.4.130

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초록· 키워드

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.
#Feature selection #Rough set theory #Differential evolution #Classification
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목차

  1. Abstract
  2. I. INTRODUCTION
  3. II. THE DIFFERENTIAL EVOLUTIONPRELIMINARIES
  4. III. DE BASED FEATURE SELECTION
  5. IV. EXPERIMENTAL RESULTS
  6. V. CONCLUSIONS
  7. REFERENCES

참고문헌 (51)

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.
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