메뉴 건너뛰기




Volumn , Issue , 2008, Pages

Fast Correlation Based Filter (FCBF) with a different search strategy

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE SELECTION (FS); SEARCH STRATEGIES;

EID: 58449122512     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCIS.2008.4717949     Document Type: Conference Paper
Times cited : (114)

References (16)
  • 1
    • 58449129600 scopus 로고    scopus 로고
    • John, G., Kohavi, R., Pfleger, K. Irrelevant features and the subset selection problem. In Proceedings of the Eleventh International Machine Learning Conference (1994) 121-129. New Brunswick, NJ: Morgan Kaufmann.
    • John, G., Kohavi, R., Pfleger, K. Irrelevant features and the subset selection problem. In Proceedings of the Eleventh International Machine Learning Conference (1994) 121-129. New Brunswick, NJ: Morgan Kaufmann.
  • 2
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • Guyon, I., Elisseef, A. An introduction to variable and feature selection. J. Machine Learning Res. 3 (2003) 1157-1182
    • (2003) J. Machine Learning Res , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseef, A.2
  • 3
    • 1942451938 scopus 로고    scopus 로고
    • L. Yu and H. Liu. Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In Proceedings of The Twentieth International Conference on Machine Leaning (ICML-03), 856-863, Washington, D.C., August 21-24, 2003.
    • L. Yu and H. Liu. Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In Proceedings of The Twentieth International Conference on Machine Leaning (ICML-03), 856-863, Washington, D.C., August 21-24, 2003.
  • 4
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Saeys, Y., Inza, I., Larrañaga, P. A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19) (2007), 2507-2517
    • (2007) Bioinformatics , vol.23 , Issue.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larrañaga, P.3
  • 7
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and minredundancy
    • Peng, H.C., Long, F., Ding, C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and minredundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp. 1226-1238, 2005
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.C.1    Long, F.2    Ding, C.3
  • 8
    • 58449119597 scopus 로고    scopus 로고
    • Molina, L.C., Belanche, L., Nebot, A. Feature Selection Algorithms: A Survey and Experimental Evaluation. 1CDM, (2002)
    • Molina, L.C., Belanche, L., Nebot, A. Feature Selection Algorithms: A Survey and Experimental Evaluation. 1CDM, (2002)
  • 9
    • 17044405923 scopus 로고    scopus 로고
    • Towards integrating feature selection algorithms for classification and clustering
    • Liu, H., Yu, L. Towards integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(3):1-12, 2005.
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.3 , pp. 1-12
    • Liu, H.1    Yu, L.2
  • 10
    • 0003368229 scopus 로고    scopus 로고
    • A comparative evaluation of sequential feature selection algorithms. In Doug Fisher and Hans-J
    • Lenz, editors, chapter 4, Springer, New York
    • Aha, D.W., Bankert, R.L. A comparative evaluation of sequential feature selection algorithms. In Doug Fisher and Hans-J. Lenz, editors, Learning from Data, chapter 4, pages 199-206. Springer, New York, 1996.
    • (1996) Learning from Data , pp. 199-206
    • Aha, D.W.1    Bankert, R.L.2
  • 11
    • 58449086908 scopus 로고    scopus 로고
    • Setiono, R., and Liu, H. A probabilistic approach to feature selection-a fitler solution. In Proceedings of International Conference on Machine Learning, 319-327 (1996).
    • Setiono, R., and Liu, H. A probabilistic approach to feature selection-a fitler solution. In Proceedings of International Conference on Machine Learning, 319-327 (1996).
  • 13
    • 0037709918 scopus 로고    scopus 로고
    • Supervised Clustering of Genes
    • 3: research 0069.1-0069.15
    • Dettling, M., Bühlmann, P. Supervised Clustering of Genes. Genome Biology (2002), 3: research 0069.1-0069.15.
    • (2002) Genome Biology
    • Dettling, M.1    Bühlmann, P.2
  • 14
    • 58449126492 scopus 로고    scopus 로고
    • Blake, C., & Merz, C. UCI repository of machine learning databases. (http://www.ics.uci.edu/mlearn/MLRepository.html.) (1998)
    • Blake, C., & Merz, C. UCI repository of machine learning databases. (http://www.ics.uci.edu/mlearn/MLRepository.html.) (1998)
  • 15
    • 33747816816 scopus 로고    scopus 로고
    • PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence
    • Li, Z.R., Lin, H.H., Han, L.Y. et al. 2006, PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence, Nucleic Acids Research, 34, W32-W37.
    • (2006) Nucleic Acids Research , vol.34
    • Li, Z.R.1    Lin, H.H.2    Han, L.Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.