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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국경영과학회 Management Science and Financial Engineering Management Science and Financial Engineering Vol.18 No.2
- 발행연도
- 2012.11
- 수록면
- 13 - 17 (5page)
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초록· 키워드
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Set covering has widely been accepted as a staple tool for feature selection in data mining. We present a generalized version of this classical combinatorial optimization model to make it better suited for the purpose and propose a surrogate relaxation-based procedure for its meta-heuristic solution. Mathematically and also numerically with experiments on 25 set covering instances, we demonstrate the utility of the proposed model and the proposed solution method.
목차
ABSTRACT
1. INTRODUCTION
2. UTILITY OF (GSC) FOR FEATURE SELECTION
3. LAGRANGIAN AND SURROGATE RELAXATIONS
4. NUMERICAL EXPERIMENTS: PRELIMINARYRESULTS AND DISCUSSIONS
5. CONCLUSIONS
REFERENCES
참고문헌 (14)
참고문헌 신청
1. [학술지(정기간행물)] - Beasley, J. E / 1987 / An Algorithm for Set Covering Problem / European Journal of Operational Research 31 : 85 ~ 93
2. [학술지(정기간행물)] - Beasley, J. E. / 1990 / A Lagrangian Heuristic for Set-covering Problems / Naval Research Logistics 37 : 151 ~ 164
3. [학술지(정기간행물)] - Beasley, J. E / 1990 / OR-Library: Distributing Test Problems by Electronic Mail / Journal of the Operational Research Society 41 : 1069 ~ 1072
4. [학술지(정기간행물)] - Boros, E. / 1997 / Logical Analysis of Numerical Data / MathematicalProgramming 79 : 163 ~ 190
5. [학술지(정기간행물)] - Boros, E. / 2000 / An Implementation of Logical Analysis of Data / IEEE Transactions on Knowledge and Data Engineering 12 (2) : 292 ~ 306
2. [학술지(정기간행물)] - Beasley, J. E. / 1990 / A Lagrangian Heuristic for Set-covering Problems / Naval Research Logistics 37 : 151 ~ 164
3. [학술지(정기간행물)] - Beasley, J. E / 1990 / OR-Library: Distributing Test Problems by Electronic Mail / Journal of the Operational Research Society 41 : 1069 ~ 1072
4. [학술지(정기간행물)] - Boros, E. / 1997 / Logical Analysis of Numerical Data / MathematicalProgramming 79 : 163 ~ 190
5. [학술지(정기간행물)] - Boros, E. / 2000 / An Implementation of Logical Analysis of Data / IEEE Transactions on Knowledge and Data Engineering 12 (2) : 292 ~ 306
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