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Volumn 9, Issue 20, 2009, Pages 3739-3745

A survey of distributed classification based ensemble data mining methods

Author keywords

Decision trees algorithm; Distributed data mining; Ensemble learning methods

Indexed keywords

DATA MINING ALGORITHM; DATA MINING METHODS; DISTRIBUTED CLASSIFICATION; DISTRIBUTED DATA MINING; ENSEMBLE LEARNING; OVERALL EFFICIENCY; PRE-DEFINED CLASS; TREES ALGORITHM;

EID: 70349814661     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2009.3739.3745     Document Type: Article
Times cited : (8)

References (40)
  • 4
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L., 1996. Bagging predictors. Mach. Learn., 24: 123-140.
    • (1996) Mach. Learn. , vol.24 , pp. 123-140
    • Breiman, L.1
  • 5
    • 0032634129 scopus 로고    scopus 로고
    • Pasting small votes for classification in large database and on-line
    • Breiman, L., 1999. Pasting small votes for classification in large database and on-line. Mach. Learn., 36: 85-103.
    • (1999) Mach. Learn. , vol.36 , pp. 85-103
    • Breiman, L.1
  • 7
    • 0242515926 scopus 로고    scopus 로고
    • Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets
    • Bryll, R., G.O. Ricardo and O. Francis, 2003. Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets. Pattern Recogn., 36: 1291-1302.
    • (2003) Pattern Recogn. , vol.36 , pp. 1291-1302
    • Bryll, R.1    Ricardo, G.O.2    Francis, O.3
  • 12
    • 0026692226 scopus 로고
    • Stacked generalization
    • David Wolpert, H., 1992. Stacked generalization. Neural Networks, 5: 241-259.
    • (1992) Neural Networks , vol.5 , pp. 241-259
    • David Wolpert, H.1
  • 13
    • 70349807948 scopus 로고    scopus 로고
    • Dagger: A new approach to combining multiple models learned from disjoint subsets
    • Davies, W. and P. Edwards, 2000. Dagger: A new approach to combining multiple models learned from disjoint subsets. Machine Learning, 2000: 1-16.
    • (2000) Machine Learning, 2000
    • Davies, W.1    Edwards, P.2
  • 14
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
    • Dietterich, T.G. and G. Thomas, 2000. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Mach. Learn., 40: 139-157.
    • (2000) Mach. Learn. , vol.40 , pp. 139-157
    • Dietterich, T.G.1    Thomas, G.2
  • 15
    • 62649176164 scopus 로고    scopus 로고
    • Learning to predict in complex biological domains
    • Eschnch, S., N.V. Chawla and L.O. Hall, 2002. Learning to predict in complex biological domains. J. Syst. Simul., 2: 1464-1471.
    • (2002) J. Syst. Simul. , vol.2 , pp. 1464-1471
    • Eschnch, S.1    Chawla, N.V.2    Hall, L.O.3
  • 17
    • 58149321460 scopus 로고
    • Boosting a weak learning algorithm by majority
    • Freund, Y., 1995. Boosting a weak learning algorithm by majority. Inform. Comput, 121: 256-258.
    • (1995) Inform. Comput , vol.121 , pp. 256-258
    • Freund, Y.1
  • 19
    • 0032139235 scopus 로고    scopus 로고
    • The random subspace method for constructing decision forests
    • Kam Ho, T., 1998. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell, 20: 832-844.
    • (1998) IEEE Trans. Pattern Anal. Mach. Intell , vol.20 , pp. 832-844
    • Kam Ho, T.1
  • 24
    • 0036495711 scopus 로고    scopus 로고
    • Boosting algorithms for parallel and distributed learning
    • Lazarevic, A. and Z. Obradovic, 2002. Boosting algorithms for parallel and distributed learning. Parallel Distributed Data Mining, 11: 203-229.
    • (2002) Parallel Distributed Data Mining , vol.11 , pp. 203-229
    • Lazarevic, A.1    Obradovic, Z.2
  • 26
    • 67149097659 scopus 로고    scopus 로고
    • Distributed data mining bibliography
    • Release
    • Liu, K., H. Kargupta and J. Ryan, 2006. Distributed data mining bibliography. Release, pp: 1-7. http://www.biostat.wustl.edu/archives/html/s-news/2003-09/msg00165.html.
    • (2006) , pp. 1-7
    • Liu, K.1    Kargupta, H.2    Ryan, J.3
  • 28
    • 26944466584 scopus 로고    scopus 로고
    • Distributed Data Mining: Algorithms, Systems and Applications
    • Data Mining Handbook, USA
    • Park, B. and H. Kargupta, 2002. Distributed Data Mining: Algorithms, Systems and Applications. Data Mining Handbook, USA.
    • (2002)
    • Park, B.1    Kargupta, H.2
  • 29
    • 1242268938 scopus 로고    scopus 로고
    • Tree induction vs. logistic regression: A learning-curve analysis
    • Perlich, C, F. Provost and J. Simonoff, 2003. Tree induction vs. logistic regression: A learning-curve analysis. J. Mach. Learn. Res., 4: 211-255.
    • (2003) J. Mach. Learn. Res. , vol.4 , pp. 211-255
    • Perlich, C.1    Provost, F.2    Simonoff, J.3
  • 31
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Robert Shapire, E., 1990. The strength of weak learnability. Mach. Learn. J., 5: 197-227.
    • (1990) Mach. Learn. J. , vol.5 , pp. 197-227
    • Robert Shapire, E.1
  • 33
    • 70349826365 scopus 로고
    • C4.5 Programs for Machine Learning
    • San Francisco, CA, USA
    • Ross Quinlan, J., 1993. C4.5 Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
    • (1993) Morgan Kaufmann Publishers Inc.
    • Ross Quinlan, J.1
  • 39
    • 25044445037 scopus 로고    scopus 로고
    • Parallelizing Boosting and Bagging
    • Queen's University, Kingston, Ontario, Canada
    • Yu, C. and D.B. Skillicorn, 2001. Parallelizing Boosting and Bagging. Queen's University, Kingston, Ontario, Canada.
    • (2001)
    • Yu, C.1    Skillicorn, D.B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.