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Volumn 3635 LNAI, Issue , 2005, Pages 180-198

Ensemble algorithms for feature selection

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; FEATURE EXTRACTION; SET THEORY; STATISTICAL METHODS; TREES (MATHEMATICS);

EID: 33646005784     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11559887_11     Document Type: Conference Paper
Times cited : (10)

References (16)
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    • Random forests
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  • 4
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Dietterich, T.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40 (2000) 139-157
    • (2000) Machine Learning , vol.40 , pp. 139-157
    • Dietterich, T.1
  • 7
    • 84947786689 scopus 로고    scopus 로고
    • Nearest neighbours in random subspaces
    • Advances in Pattern Recognition Springer
    • Ho, T.: Nearest neighbours in random subspaces. In: Advances in Pattern Recognition. Volume 1451 of Lecture Notes in Computer Science., Springer (1998) 640-648
    • (1998) Lecture Notes in Computer Science , vol.1451 , pp. 640-648
    • Ho, T.1
  • 8
    • 85065703189 scopus 로고    scopus 로고
    • Correlation-based feature selection for discrete and numeric class machine learning
    • Hall, M.: Correlation-based feature selection for discrete and numeric class machine learning. In: 17th International Conference on Machine Learning. (2000) 359-366
    • (2000) 17th International Conference on Machine Learning , pp. 359-366
    • Hall, M.1
  • 9
    • 85099325734 scopus 로고
    • Irrelevant features and the subset selection problem
    • Cohen, W., Hirsh, H., eds.: Morgan Kaufmann
    • John, G., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In Cohen, W., Hirsh, H., eds.: Machine Learning, Morgan Kaufmann (1994) 121-129
    • (1994) Machine Learning , pp. 121-129
    • John, G.1    Kohavi, R.2    Pfleger, K.3
  • 10
    • 84981715725 scopus 로고    scopus 로고
    • Information gain, correlation and support vector machines
    • Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L., eds.: Springer In Press
    • Roobaert, D., Karakoulas, G., Chawla, N.: Information gain, correlation and support vector machines. In Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L., eds.: Feature Extraction, Foundations and Applications, Springer (2005) In Press.
    • (2005) Feature Extraction, Foundations and Applications
    • Roobaert, D.1    Karakoulas, G.2    Chawla, N.3
  • 11
    • 1942451938 scopus 로고    scopus 로고
    • Feature selection for high-dimensional data: A fast correlation-based filter solution
    • AAAI
    • Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Machine Learning, AAAI (2003) 856-863
    • (2003) Machine Learning , pp. 856-863
    • Yu, L.1    Liu, H.2
  • 13
    • 0003609317 scopus 로고    scopus 로고
    • Parcel: Feature subset selection in variable cost domains
    • Cambridge University Engineering Department
    • Scott, M., Niranjan, M., Prager, R.: Parcel: feature subset selection in variable cost domains. Technical report, Cambridge University Engineering Department (1998)
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    • Scott, M.1    Niranjan, M.2    Prager, R.3
  • 14
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    • Tree-based ensembles with dynamic soft feature selection
    • Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L., eds.: Springer In Press
    • Borisov, A., Eruhimov, V., Tuv, E.: Tree-based ensembles with dynamic soft feature selection. In Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L., eds.: Feature Extraction, Foundations and Applications, Springer (2005) In Press.
    • (2005) Feature Extraction, Foundations and Applications
    • Borisov, A.1    Eruhimov, V.2    Tuv, E.3
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    • Multivariate adaptive regression splines
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.