메뉴 건너뛰기




Volumn 15, Issue 5, 2011, Pages 671-693

Component-based decision trees for classification

Author keywords

Classification; data mining; decision tree; induction; open source platform; reusable components

Indexed keywords

ALGORITHM DESIGN; ALGORITHM PERFORMANCE; BENCHMARK DATASETS; BLACK BOXES; COMPONENT BASED; DATA MINING ALGORITHM; DATA SETS; DECISION-TREE ALGORITHM; END USERS; INDUCTION; INDUCTION PROCESS; JOINT INFLUENCE; NEW COMPONENTS; OPEN-SOURCE; REUSABLE COMPONENTS; STATISTICAL SIGNIFICANCE; TESTING ALGORITHM; TREE NODES;

EID: 80052637880     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2011-0489     Document Type: Conference Paper
Times cited : (17)

References (30)
  • 2
    • 0033570831 scopus 로고    scopus 로고
    • Combined 5 ?2 cv F test for comparing supervised classification learning algorithms
    • E. Alpaydin, Combined 5 ?2 cv F test for comparing supervised classification learning algorithms, Neural Computation 11 (1999), 1885-1892.
    • (1999) Neural Computation , vol.11 , pp. 1885-1892
    • Alpaydin, E.1
  • 7
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • T. Dietterich, Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computation 10 (1998), 1895-1923. (Pubitemid 128463689)
    • (1998) Neural Computation , vol.10 , Issue.7 , pp. 1895-1923
    • Dietterich, T.G.1
  • 8
    • 80052608614 scopus 로고    scopus 로고
    • Decision tree toolkit: A component-based library of decision tree algorithms
    • N. Drossos, A. Papagelis and D. Kalles, Decision tree toolkit: A component-based library of decision tree algorithms, Lecture Notes in Computer Science (2000), 381-387.
    • (2000) Lecture Notes in Computer Science , pp. 381-387
    • Drossos, N.1    Papagelis, A.2    Kalles, D.3
  • 9
    • 0003024008 scopus 로고
    • On the hand ling of continuous-valued attributes in decision tree generation
    • U. M. Fayyad and K. B. Irani, On the hand ling of continuous-valued attributes in decision tree generation, Machine Learning 8 (1992), 87-102.
    • (1992) Machine Learning , vol.8 , pp. 87-102
    • Fayyad, U.M.1    Irani, K.B.2
  • 11
    • 23044519492 scopus 로고    scopus 로고
    • Rain forest: A framework for fast decision tree construction of large datasets
    • J. Gehrke, R. Ramakrishnan and V. Ganti, RainForest: A Framework for fast decision tree construction of large datasets, Data Mining and Knowledge Discovery 4 (2000), 127-168.
    • (2000) Data Mining and Knowledge Discovery , vol.4 , pp. 127-168
    • Gehrke, J.1    Ramakrishnan, R.2    Ganti, V.3
  • 12
    • 0000661829 scopus 로고
    • An exploratory technique for investigating large quantities of categorical data
    • G. V. Kass, An exploratory technique for investigating large quantities of categorical data, Applied Statistics 29 (1980), 119-127.
    • (1980) Applied Statistics , vol.29 , pp. 119-127
    • Kass, G.V.1
  • 13
    • 85156137079 scopus 로고    scopus 로고
    • Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid
    • R. Kohavi, Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid, in: KDD 96, 1999, pp. 202-207.
    • (1999) KDD , vol.96 , pp. 202-207
    • Kohavi, R.1
  • 14
    • 0031312210 scopus 로고    scopus 로고
    • Split selection methods for classification trees
    • W. Y. Loh and Y. S. Shih, Split selection methods for classification trees, Statistica Sinica 7 (1997), 815-840.
    • (1997) Statistica Sinica , vol.7 , pp. 815-840
    • Loh, W.Y.1    Shih, Y.S.2
  • 15
    • 0025798330 scopus 로고
    • A distance-based attribute selection measure for decision tree induction
    • R. L. Mantaras, A distance-based attribute selection measure for decision tree induction, Machine Learning 6 (1991), 81-92.
    • (1991) Machine Learning , vol.6 , pp. 81-92
    • Mantaras, R.L.1
  • 17
    • 0002431740 scopus 로고    scopus 로고
    • Automatic construction of decision trees fromdata: Amultidisciplinary survey
    • S. K. Murthy,Automatic construction of decision trees fromdata: Amultidisciplinary survey, Data Mining and Knowledge Discovery 2, 345-389.
    • Data Mining and Knowledge Discovery , vol.2 , pp. 345-389
    • Murthy, S.K.1
  • 18
    • 33744584654 scopus 로고
    • Induction of decision trees
    • J. R. Quinlan, Induction of decision trees, Machine Learning 1 (1986), 81-106.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 20
    • 2642574503 scopus 로고    scopus 로고
    • R. Development Core Team R R Foundation for Statistical Computing, Vienna, Austria
    • R. Development Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2008 [http://www. R-project. org].
    • (2008) A Language and Environment for Statistical Computing
  • 22
    • 0002534234 scopus 로고    scopus 로고
    • Salzberg,On comparing classifiers: Acritique of current research and methods
    • S. Salzberg,On comparing classifiers: Acritique of current research and methods, Data Mining and Knowledge Discovery 1 (1999), 1-12.
    • (1999) Data Mining and Knowledge Discovery , vol.1 , pp. 1-12
  • 25
    • 0036147388 scopus 로고    scopus 로고
    • Is this a pattern?
    • DOI 10.1109/52.976942
    • T. Winn and P. Calder, Is this a pattern? IEEE Software 19 (2002), 59-66. (Pubitemid 34069371)
    • (2002) IEEE Software , vol.19 , Issue.1 , pp. 59-66
    • Winn, T.1    Calder, P.2
  • 27
    • 0000459353 scopus 로고    scopus 로고
    • The Lack of a Priori Distinctions between Learning Algorithms
    • D. H. Wolpert, The lack of a priori distinctions between learning algorithms, Neural Computation 8 (1996), 1341-1390. (Pubitemid 126449973)
    • (1996) Neural Computation , vol.8 , Issue.7 , pp. 1341-1390
    • Wolpert, D.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.