메뉴 건너뛰기




Volumn , Issue , 2009, Pages 567-575

Consensus group stable feature selection

Author keywords

Ensemble; Feature selection; High dimensional data; Small sample; Stability

Indexed keywords

ENSEMBLE; FEATURE GROUPS; FEATURE SELECTION; FEATURE SELECTION ALGORITHM; GENERALIZATION PERFORMANCE; HIGH-DIMENSIONAL; HIGH-DIMENSIONAL DATA; REAL WORLD DATA; SAMPLE SIZES; SMALL SAMPLE; SMALL SAMPLE SIZE; SMALL SAMPLES; SOURCE CODES; SYNTHETIC DATASETS; TRAINING SAMPLE;

EID: 70350686854     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557084     Document Type: Conference Paper
Times cited : (130)

References (27)
  • 1
    • 14344265818 scopus 로고    scopus 로고
    • A. Appice, M. Ceci, S. Rawles, and P. Flach. Redundant feature elimination for multi-class problems. In Proceedings of the 21st International Conference on Machine learning, pages 33{40, 2004.
    • A. Appice, M. Ceci, S. Rawles, and P. Flach. Redundant feature elimination for multi-class problems. In Proceedings of the 21st International Conference on Machine learning, pages 33{40, 2004.
  • 2
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-142, 1999.
    • (1999) Machine Learning , vol.36 , pp. 105-142
    • Bauer, E.1    Kohavi, R.2
  • 7
    • 2942731012 scopus 로고    scopus 로고
    • An extensive empirical study of feature selection metrics for text classification
    • G. Forman. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3:1289-1305, 2003.
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1289-1305
    • Forman, G.1
  • 8
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • T. R. Golub, D. K. Slonim, P. Tamayo, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531-537, 1999.
    • (1999) Science , vol.286 , pp. 531-537
    • Golub, T.R.1    Slonim, D.K.2    Tamayo, P.3
  • 9
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46:389-422, 2002.
    • (2002) Machine Learning , vol.46 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 11
    • 34248647608 scopus 로고    scopus 로고
    • Stability of feature selection algorithms: A study on high-dimensional spaces
    • A. Kalousis, J. Prados, and M. Hilario. Stability of feature selection algorithms: a study on high-dimensional spaces. Knowledge and Information Systems, 12:95-116, 2007.
    • (2007) Knowledge and Information Systems , vol.12 , pp. 95-116
    • Kalousis, A.1    Prados, J.2    Hilario, M.3
  • 13
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
    • (1997) Artificial Intelligence , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 14
    • 7244248755 scopus 로고    scopus 로고
    • A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression
    • T. Li, C. Zhang, and M. Ogihara. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20:2429-2437, 2004.
    • (2004) Bioinformatics , vol.20 , pp. 2429-2437
    • Li, T.1    Zhang, C.2    Ogihara, M.3
  • 15
    • 0038021028 scopus 로고    scopus 로고
    • A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns
    • H. Liu, J. Li, and L. Wong. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Informatics, 13:51-60, 2002.
    • (2002) Genome Informatics , vol.13 , pp. 51-60
    • Liu, H.1    Li, J.2    Wong, L.3
  • 16
    • 17044405923 scopus 로고    scopus 로고
    • Toward integrating feature selection algorithms for classification and clustering
    • H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4):491-502, 2005.
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.4 , pp. 491-502
    • Liu, H.1    Yu, L.2
  • 18
    • 0013161560 scopus 로고    scopus 로고
    • On feature selection: Learning with exponentially many irrelevant features as training examples
    • A. Y. Ng. On feature selection: learning with exponentially many irrelevant features as training examples. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 404-412, 1998.
    • (1998) Proceedings of the Fifteenth International Conference on Machine Learning , pp. 404-412
    • Ng, A.Y.1
  • 19
    • 0035908491 scopus 로고    scopus 로고
    • Phases of biomarker development for early detection of cancer
    • M. S. Pepe, R. Etzioni, Z. Feng, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst, 93:1054-1060, 2001.
    • (2001) J Natl Cancer Inst , vol.93 , pp. 1054-1060
    • Pepe, M.S.1    Etzioni, R.2    Feng, Z.3
  • 20
    • 0037116832 scopus 로고    scopus 로고
    • Use of proteomic patterns in serum to identify ovarian cancer
    • E. F. Petricoin, A. M. Ardekani, B. A. Hitt, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359:572-577, 2002.
    • (2002) Lancet , vol.359 , pp. 572-577
    • Petricoin, E.F.1    Ardekani, A.M.2    Hitt, B.A.3
  • 21
    • 56049119676 scopus 로고    scopus 로고
    • Robust feature selection using ensemble feature selection techniques
    • Y. Saeys, T. Abeel, and Y. V. Peer. Robust feature selection using ensemble feature selection techniques. In Proceedings of the ECML Confernce, pages 313-325, 2008.
    • (2008) Proceedings of the ECML Confernce , pp. 313-325
    • Saeys, Y.1    Abeel, T.2    Peer, Y.V.3
  • 23
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles - a knowledge reuse framework for combining multiple partitions
    • A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3:583-617, 2002.
    • (2002) Journal of Machine Learning Research , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2
  • 27
    • 25144492516 scopus 로고    scopus 로고
    • Efficient feature selection via analysis of relevance and redundancy
    • L. Yu and H. Liu. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5:1205-1224, 2004.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1205-1224
    • Yu, L.1    Liu, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.