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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국마린엔지니어링학회 Journal of Advanced Marine Engineering and Technology (JAMET) 한국마린엔지니어링학회지 제40권 제5호
- 발행연도
- 2016.6
- 수록면
- 437 - 446 (10page)
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초록· 키워드
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Feature selection has become an essential technique to reduce the dimensionality of data sets. Many features are frequently irrelevant or redundant for the classification tasks. The purpose of feature selection is to select relevant features and remove irrelevant and redundant features. Applications of the feature selection range from text processing, face recognition, bioinformatics, speaker verification, and medical diagnosis to financial domains. In this study, we focus on filter methods based on information entropy : IG (Information Gain), FCBF (Fast Correlation Based Filter), and mRMR (minimum Redundancy Maximum Relevance). FCBF has the advantage of reducing computational burden by eliminating the redundant features that satisfy the condition of approximate Markov blanket. However, FCBF considers only the relevance between the feature and the class in order to select the best features, thus failing to take into consideration the interaction between features. In this paper, we propose an improved FCBF to overcome this shortcoming. We also perform a comparative study to evaluate the performance of the proposed method.
목차
Abstract
1. Introduction
2. Methods
3. Computational Results
4. Conclusions
References
참고문헌 (29)
참고문헌 신청
[학위논문] - M. Hall / 1999 / Correlation-based feature selection for machine learning / 박사
[학술대회논문] - Z. Zhao / 2007 / Searching for interacting features / International Joint Conference on Artificial Intelligence 7 : 1156 ~ 1161
[학술지(정기간행물)] - I. Guyon / 2002 / Gene selection for cancer classification using support vector machines / Machine Learning 46 : 389 ~ 422
[학술지(정기간행물)] - S. Maldonado / 2011 / Simultaneous feature selection and classification using kernel-penalized support vector machines / Information Sciences 181 (1) : 115 ~ 128
[학술지(정기간행물)] - 배재국 / 2016 / Subset selection in multiple linear regression: An improved Tabu search / 한국마린엔지니어링학회지 40 (2) : 138 ~ 145
[학술대회논문] - Z. Zhao / 2007 / Searching for interacting features / International Joint Conference on Artificial Intelligence 7 : 1156 ~ 1161
[학술지(정기간행물)] - I. Guyon / 2002 / Gene selection for cancer classification using support vector machines / Machine Learning 46 : 389 ~ 422
[학술지(정기간행물)] - S. Maldonado / 2011 / Simultaneous feature selection and classification using kernel-penalized support vector machines / Information Sciences 181 (1) : 115 ~ 128
[학술지(정기간행물)] - 배재국 / 2016 / Subset selection in multiple linear regression: An improved Tabu search / 한국마린엔지니어링학회지 40 (2) : 138 ~ 145
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UCI(KEPA) : I410-ECN-0101-2017-559-000803559