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논문 기본 정보
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- 학술저널
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- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems 제14권 제2호
- 발행연도
- 2015.6
- 수록면
- 210 - 219 (10page)
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A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using wellknown classifiers.
목차
ABSTRACT
1. INTRODUCTION
2. BAYESIAN NETWORK AND MARKOV BLANKET
3. MARKOV BLANKET DISCOVERY ALGORITHMS AS FEATURE SELECTION METHODS
4. OTHER FEATURE SELECTION METHODS AND CLASSIFIERS UNDER CONSIDERATION
5. EXPERIMENTS
6. CONCLUSION
REFERENCES
참고문헌 (24)
참고문헌 신청
[학술대회논문] - Aliferis, C. F. / 2003 / Hiton: a novel Markov blanket algorithm for optimal variable selection / American Medical Informatics Association Annual Symposium Proceedings : 21 ~ 25
[학술대회논문] - Aliferis, C. F. / 2003 / Causal explorer: a causal probabilistic network learning toolkit for biomedical discovery / METMBS Conference 3 : 371 ~ 376
[학술대회논문] - Ding, C. / 2002 / Analysis of gene expression profiles:class discovery and leaf ordering / Proceedings of the 6th Annual International Conference on Computational Biology : 127 ~ 136
[학술지(정기간행물)] - Ding, C. / 2005 / Minimum redundancy feature selection from microarray gene expression data / Journal of Bioinformatics and Computational Biology 3 (2) : 185 ~ 205
[학술지(정기간행물)] - Fix, E. / 1989 / Discriminatory analysis-nonparametric discrimination : consistency properties / International Statistical Review 57 (3) : 238 ~ 247
[학술대회논문] - Aliferis, C. F. / 2003 / Causal explorer: a causal probabilistic network learning toolkit for biomedical discovery / METMBS Conference 3 : 371 ~ 376
[학술대회논문] - Ding, C. / 2002 / Analysis of gene expression profiles:class discovery and leaf ordering / Proceedings of the 6th Annual International Conference on Computational Biology : 127 ~ 136
[학술지(정기간행물)] - Ding, C. / 2005 / Minimum redundancy feature selection from microarray gene expression data / Journal of Bioinformatics and Computational Biology 3 (2) : 185 ~ 205
[학술지(정기간행물)] - Fix, E. / 1989 / Discriminatory analysis-nonparametric discrimination : consistency properties / International Statistical Review 57 (3) : 238 ~ 247
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UCI(KEPA) : I410-ECN-0101-2016-530-001528107