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Volumn , Issue , 2014, Pages 2645-2652

Towards generating random forests via extremely randomized trees

Author keywords

[No Author keywords available]

Indexed keywords

RANDOM FORESTS;

EID: 84908494818     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2014.6889537     Document Type: Conference Paper
Times cited : (26)

References (36)
  • 2
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • -, "Bagging predictors", Machine learning, vol. 24, no. 2, pp. 123-140, 1996.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 3
    • 0002978642 scopus 로고    scopus 로고
    • Experiments with a new boosting algorithm
    • Y. Freund, R. E. Schapire et al., "Experiments with a new boosting algorithm", in ICML, vol. 96, 1996, pp. 148-156.
    • (1996) ICML , vol.96 , pp. 148-156
    • Freund, Y.1    Schapire, R.E.2
  • 5
    • 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, vol. 36, no. 1-2, pp. 105-139, 1999.
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 6
    • 80053403826 scopus 로고    scopus 로고
    • Ensemble methods in machine learning
    • Springer
    • T. G. Dietterich, "Ensemble methods in machine learning", in Multiple classifier systems. Springer, 2000, pp. 1-15.
    • (2000) Multiple Classifier Systems , pp. 1-15
    • Dietterich, T.G.1
  • 8
    • 84983110889 scopus 로고
    • A desicion-theoretic generalization of online learning and an application to boosting
    • Springer
    • Y. Freund and R. E. Schapire, "A desicion-theoretic generalization of online learning and an application to boosting", in Computational learning theory. Springer, 1995, pp. 23-37.
    • (1995) Computational Learning Theory , pp. 23-37
    • Freund, Y.1    Schapire, R.E.2
  • 9
    • 0002595663 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • R. E. Schapire and Y. Freund, "Boosting the margin: a new explanation for the effectiveness of voting methods", The Annals of Statistics, vol. 26, pp. 322-330, 1998.
    • (1998) The Annals of Statistics , vol.26 , pp. 322-330
    • Schapire, R.E.1    Freund, Y.2
  • 10
    • 0036083445 scopus 로고    scopus 로고
    • A data complexity analysis of comparative advantages of decision forest constructors
    • T. K. Ho, "A data complexity analysis of comparative advantages of decision forest constructors", Pattern Analysis & Applications, vol. 5, no. 2, pp. 102-112, 2002.
    • (2002) Pattern Analysis & Applications , vol.5 , Issue.2 , pp. 102-112
    • Ho, T.K.1
  • 11
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman, "Random forests", Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 12
    • 13244289883 scopus 로고    scopus 로고
    • Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes
    • H. Jiang, Y. Deng, H.-S. Chen, L. Tao, Q. Sha, J. Chen, C.-J. Tsai, and S. Zhang, "Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes", BMC bioinformatics, vol. 5, no. 1, p. 81, 2004.
    • (2004) BMC Bioinformatics , vol.5 , Issue.1 , pp. 81
    • Jiang, H.1    Deng, Y.2    Chen, H.-S.3    Tao, L.4    Sha, Q.5    Chen, J.6    Tsai, C.-J.7    Zhang, S.8
  • 15
    • 13344278660 scopus 로고    scopus 로고
    • Random forest classifier for remote sensing classification
    • M. Pal, "Random forest classifier for remote sensing classification", International Journal of Remote Sensing, vol. 26, no. 1, pp. 217-222, 2005.
    • (2005) International Journal of Remote Sensing , vol.26 , Issue.1 , pp. 217-222
    • Pal, M.1
  • 19
    • 54249099241 scopus 로고    scopus 로고
    • Consistency of random forests and other averaging classifiers
    • G. Biau, L. Devroye, and G. Lugosi, "Consistency of random forests and other averaging classifiers", The Journal of Machine Learning Research, vol. 9, pp. 2015-2033, 2008.
    • (2008) The Journal of Machine Learning Research , vol.9 , pp. 2015-2033
    • Biau, G.1    Devroye, L.2    Lugosi, G.3
  • 20
    • 33745653724 scopus 로고    scopus 로고
    • Random forests and adaptive nearest neighbors
    • Y. Lin and Y. Jeon, "Random forests and adaptive nearest neighbors", Journal of the American Statistical Association, vol. 101, no. 474, pp. 578-590, 2006.
    • (2006) Journal of the American Statistical Association , vol.101 , Issue.474 , pp. 578-590
    • Lin, Y.1    Jeon, Y.2
  • 21
    • 33847236254 scopus 로고    scopus 로고
    • Multivariate feature selection and hierarchical classification for infrared spectroscopy: Serumbased detection of bovine spongiform encephalopathy
    • B. H. Menze, W. Petrich, and F. A. Hamprecht, "Multivariate feature selection and hierarchical classification for infrared spectroscopy: serumbased detection of bovine spongiform encephalopathy", Analytical and bioanalytical chemistry, vol. 387, no. 5, pp. 1801-1807, 2007.
    • (2007) Analytical and Bioanalytical Chemistry , vol.387 , Issue.5 , pp. 1801-1807
    • Menze, B.H.1    Petrich, W.2    Hamprecht, F.A.3
  • 23
    • 68949140728 scopus 로고    scopus 로고
    • A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data
    • B. H. Menze, B. M. Kelm, R. Masuch, U. Himmelreich, P. Bachert, W. Petrich, and F. A. Hamprecht, "A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data", BMC bioinformatics, vol. 10, no. 1, p. 213, 2009.
    • (2009) BMC Bioinformatics , vol.10 , Issue.1 , pp. 213
    • Menze, B.H.1    Kelm, B.M.2    Masuch, R.3    Himmelreich, U.4    Bachert, P.5    Petrich, W.6    Hamprecht, F.A.7
  • 24
    • 68949154557 scopus 로고    scopus 로고
    • Feature selection with ensembles, artificial variables, and redundancy elimination
    • E. Tuv, A. Borisov, G. Runger, and K. Torkkola, "Feature selection with ensembles, artificial variables, and redundancy elimination", The Journal of Machine Learning Research, vol. 10, pp. 1341-1366, 2009.
    • (2009) The Journal of Machine Learning Research , vol.10 , pp. 1341-1366
    • Tuv, E.1    Borisov, A.2    Runger, G.3    Torkkola, K.4
  • 26
    • 26944501740 scopus 로고    scopus 로고
    • Bias-variance analysis of support vector machines for the development of svm-based ensemble methods
    • G. Valentini and T. G. Dietterich, "Bias-variance analysis of support vector machines for the development of svm-based ensemble methods", The Journal of Machine Learning Research, vol. 5, pp. 725-775, 2004.
    • (2004) The Journal of Machine Learning Research , vol.5 , pp. 725-775
    • Valentini, G.1    Dietterich, T.G.2
  • 27
    • 44449124996 scopus 로고    scopus 로고
    • Rotboost: A technique for combining rotation forest and adaboost
    • C.-X. Zhang and J.-S. Zhang, "Rotboost: A technique for combining rotation forest and adaboost", Pattern Recognition Letters, vol. 29, no. 10, pp. 1524-1536, 2008.
    • (2008) Pattern Recognition Letters , vol.29 , Issue.10 , pp. 1524-1536
    • Zhang, C.-X.1    Zhang, J.-S.2
  • 28
    • 33646430006 scopus 로고    scopus 로고
    • Extremely randomized trees
    • P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees", Machine learning, vol. 63, no. 1, pp. 3-42, 2006.
    • (2006) Machine Learning , vol.63 , Issue.1 , pp. 3-42
    • Geurts, P.1    Ernst, D.2    Wehenkel, L.3
  • 29
    • 0001942829 scopus 로고
    • Neural networks and the bias/variance dilemma
    • S. Geman, E. Bienenstock, and R. Doursat, "Neural networks and the bias/variance dilemma", Neural computation, vol. 4, no. 1, pp. 1-58, 1992.
    • (1992) Neural Computation , vol.4 , Issue.1 , pp. 1-58
    • Geman, S.1    Bienenstock, E.2    Doursat, R.3
  • 30
    • 84992322729 scopus 로고
    • Error-correcting output coding corrects bias and variance
    • E. B. Kong and T. G. Dietterich, "Error-correcting output coding corrects bias and variance." in ICML, 1995, pp. 313-321.
    • (1995) ICML , pp. 313-321
    • Kong, E.B.1    Dietterich, T.G.2
  • 31
    • 0002872346 scopus 로고    scopus 로고
    • Bias plus variance decomposition for zero-one loss functions
    • R. Kohavi, D. H. Wolpert et al., "Bias plus variance decomposition for zero-one loss functions", in ICML, 1996, pp. 275-283.
    • (1996) ICML , pp. 275-283
    • Kohavi, R.1    Wolpert, D.H.2
  • 32
    • 21744462998 scopus 로고    scopus 로고
    • On bias, variance, 0/1łloss, and the curse-of-dimensionality
    • J. H. Friedman, "On bias, variance, 0/1łloss, and the curse-of-dimensionality", Data mining and knowledge discovery, vol. 1, no. 1, pp. 55-77, 1997.
    • (1997) Data Mining and Knowledge Discovery , vol.1 , Issue.1 , pp. 55-77
    • Friedman, J.H.1
  • 33
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifier (with discussion and a rejoinder by the author)
    • L. Breiman, "Arcing classifier (with discussion and a rejoinder by the author)", The annals of statistics, vol. 26, no. 3, pp. 801-849, 1998.
    • (1998) The Annals of Statistics , vol.26 , Issue.3 , pp. 801-849
    • Breiman, L.1
  • 34
    • 0037403462 scopus 로고    scopus 로고
    • Variance and bias for general loss functions
    • G. M. James, "Variance and bias for general loss functions", Machine Learning, vol. 51, no. 2, pp. 115-135, 2003.
    • (2003) Machine Learning , vol.51 , Issue.2 , pp. 115-135
    • James, G.M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.