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Volumn 45, Issue 9, 2012, Pages 3084-3104

An extensive experimental comparison of methods for multi-label learning

Author keywords

Comparison of multi label learning methods; Multi label classification; Multi label ranking

Indexed keywords

APPLICATION DOMAINS; BENCHMARK DATASETS; CLUSTERING TREES; DATA SETS; DIFFERENT DOMAINS; EVALUATION MEASURES; EXPERIMENTAL COMPARISON; LEARNING METHODS; MACHINE LEARNING METHODS; MULTI-LABEL; RANDOM FORESTS; RESEARCH COMMUNITIES; STATISTICAL SIGNIFICANCE;

EID: 84861617363     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.03.004     Document Type: Conference Paper
Times cited : (683)

References (47)
  • 3
    • 33947681316 scopus 로고    scopus 로고
    • ML-kNN: A lazy learning approach to multi-label learning
    • M.L. Zhang, and Z.H. Zhou ML-kNN: a lazy learning approach to multi-label learning Pattern Recognition 40 7 2007 2038 2048
    • (2007) Pattern Recognition , vol.40 , Issue.7 , pp. 2038-2048
    • Zhang, M.L.1    Zhou, Z.H.2
  • 6
    • 0142228873 scopus 로고    scopus 로고
    • A family of additive online algorithms for category ranking
    • K. Crammer, and Y. Singer A family of additive online algorithms for category ranking Journal of Machine Learning Research 3 2003 1025 1058
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1025-1058
    • Crammer, K.1    Singer, Y.2
  • 7
    • 33748366796 scopus 로고    scopus 로고
    • Multi-label neural networks with applications to functional genomics and text categorization
    • M.L. Zhang, and Z.H. Zhou Multi-label neural networks with applications to functional genomics and text categorization IEEE Transactions on Knowledge and Data Engineering 18 10 2006 1338 1351
    • (2006) IEEE Transactions on Knowledge and Data Engineering , vol.18 , Issue.10 , pp. 1338-1351
    • Zhang, M.L.1    Zhou, Z.H.2
  • 8
    • 0033905095 scopus 로고    scopus 로고
    • Boostexter: A boosting-based system for text categorization
    • R.E. Schapire, and Y. Singer Boostexter: a boosting-based system for text categorization Machine Learning 39 2000 135 168
    • (2000) Machine Learning , vol.39 , pp. 135-168
    • Schapire, R.E.1    Singer, Y.2
  • 14
    • 51349159085 scopus 로고    scopus 로고
    • Probability estimates for multi-class classification by pairwise coupling
    • T.-F. Wu, C.-J. Lin, and R.C. Weng Probability estimates for multi-class classification by pairwise coupling Journal of Machine Learning Research 5 2004 975 1005
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 975-1005
    • Wu, T.-F.1    Lin, C.-J.2    Weng, R.C.3
  • 19
    • 68949141664 scopus 로고    scopus 로고
    • Combining instance-based learning and logistic regression for multilabel classification
    • W. Cheng, and E. Hullermeier Combining instance-based learning and logistic regression for multilabel classification Machine Learning 76 2009 211 225
    • (2009) Machine Learning , vol.76 , pp. 211-225
    • Cheng, W.1    Hullermeier, E.2
  • 21
    • 7444230008 scopus 로고    scopus 로고
    • Discriminative methods for multi-labeled classification
    • Springer, Berlin/Heidelberg
    • S. Godbole, S. Sarawagi, Discriminative methods for multi-labeled classification, in: Advances in Knowledge Discovery and Data Mining, Springer, Berlin/Heidelberg, 2004, pp. 22-30.
    • (2004) Advances in Knowledge Discovery and Data Mining , pp. 22-30
    • Godbole, S.1    Sarawagi, S.2
  • 26
    • 77649237436 scopus 로고    scopus 로고
    • Efficient voting prediction for pairwise multilabel classification
    • E.L. Mencía, S.-H. Park, and J. Fürnkranz Efficient voting prediction for pairwise multilabel classification Neurocomputing 73 2010 1164 1176
    • (2010) Neurocomputing , vol.73 , pp. 1164-1176
    • Mencía, E.L.1    Park, S.-H.2    Fürnkranz, J.3
  • 35
  • 38
    • 0003505985 scopus 로고    scopus 로고
    • CDC/National Center for Health Statistics Ninth Revision, Clinical Modification (ICD-9-CM)
    • CDC/National Center for Health Statistics, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), 〈 http://www.cdc.gov/nchs/icd/icd9cm.htm 〉 (2011).
    • (2011) International Classification of Diseases
  • 41
    • 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 1 1999 105 139
    • (1999) Machine Learning , vol.36 , Issue.1 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 42
    • 0001837148 scopus 로고
    • A comparison of alternative tests of significance for the problem of m rankings
    • M. Friedman A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematical Statistics 11 1940 86 92
    • (1940) Annals of Mathematical Statistics , vol.11 , pp. 86-92
    • Friedman, M.1
  • 44
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 45
    • 0001750957 scopus 로고
    • Approximations of the critical region of the Friedman statistic
    • R.L. Iman, and J.M. Davenport Approximations of the critical region of the Friedman statistic Communications in Statistics 1980 571 595
    • (1980) Communications in Statistics , pp. 571-595
    • Iman, R.L.1    Davenport, J.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.