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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보통신학회JICCE Journal of information and communication convergence engineering Journal of information and communication convergence engineering Vol.13 No.2
- 발행연도
- 2015.6
- 수록면
- 113 - 122 (10page)
이용수
초록· 키워드
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Feature subset selection is as a pre-processing step in learning algorithms. In this paper, we propose an efficient algorithm, ModifiedFAST, for feature subset selection. This algorithm is suitable for text datasets, and uses the concept of information gain to remove irrelevant and redundant features. A new optimal value of the threshold for symmetric uncertainty, used to identify relevant features, is found. The thresholds used by previous feature selection algorithms such as FAST, Relief, and CFS were not optimal. It has been proven that the threshold value greatly affects the percentage of selected features and the classification accuracy. A new performance unified metric that combines accuracy and the number of features selected has been proposed and applied in the proposed algorithm. It was experimentally shown that the percentage of selected features obtained by the proposed algorithm was lower than that obtained using existing algorithms in most of the datasets. The effectiveness of our algorithm on th optimal threshold was statistically validated with other algorithms.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. RELATED WORK
Ⅲ. FEATURE SUBSET SELECTION METHOD
Ⅳ. METHODOLOGY AND ANALYSIS
Ⅴ. EMPIRICAL STUDY
Ⅵ. RESULTS AND ANALYSIS
Ⅶ. CONCLUSION
REFERENCES
참고문헌 (28)
참고문헌 신청
[단행본] - W. B. Powell / 2007 / Approximate Dynamic Programming: Solving the Curses of Dimensionality / Wiley-Interscience
[학술대회논문] - J. Huang / 2006 / A filter approach to feature selection based on mutual information / Proceedings of the 5th IEEE International Conference on Cognitive Informatics (ICCI) : 84 ~ 89
[학술대회논문] - M. A. Hall / 1999 / Feature selection for machine learning : comparing a correlation-based filter approach to the wrapper / Proceedings of the 12th International Florida AI Research Society Conference : 235 ~ 239
[학술지(정기간행물)] - R. Kohavi / 1997 / Wrappers for feature subset selection / Artificial Intelligence 97 (1) : 273 ~ 324
[학술대회논문] - S. Das / 2001 / Filters, wrappers and a boosting-based hybrid for feature selection / Proceedings of the 18th International Conference on Machine Learning (ICML2001) : 74 ~ 81
[학술대회논문] - J. Huang / 2006 / A filter approach to feature selection based on mutual information / Proceedings of the 5th IEEE International Conference on Cognitive Informatics (ICCI) : 84 ~ 89
[학술대회논문] - M. A. Hall / 1999 / Feature selection for machine learning : comparing a correlation-based filter approach to the wrapper / Proceedings of the 12th International Florida AI Research Society Conference : 235 ~ 239
[학술지(정기간행물)] - R. Kohavi / 1997 / Wrappers for feature subset selection / Artificial Intelligence 97 (1) : 273 ~ 324
[학술대회논문] - S. Das / 2001 / Filters, wrappers and a boosting-based hybrid for feature selection / Proceedings of the 18th International Conference on Machine Learning (ICML2001) : 74 ~ 81
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