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Volumn 2000-January, Issue , 2000, Pages 40-43

Knowledge pruning in decision trees

Author keywords

Classification tree analysis; Data mining; Decision trees; Entropy; Instruction sets; Machine learning algorithms; Stress; Temperature; Testing; Vehicles

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; ENTROPY; LEARNING ALGORITHMS; LEARNING SYSTEMS; SEMANTICS; STRESSES; TEMPERATURE; TESTING; TREES (MATHEMATICS); VEHICLES;

EID: 0142222832     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/TAI.2000.889844     Document Type: Conference Paper
Times cited : (4)

References (8)
  • 1
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    • Decision Tree Reduction
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    • (1990) J. ACM , vol.37 , Issue.4 , pp. 815-842
    • Cockett, J.R.B.1    Herrera, J.A.2
  • 2
    • 84976766582 scopus 로고
    • Overfitting and Undercomputing in Machine Learning
    • Diettrich, T.: Overfitting and Undercomputing in Machine Learning, ACM Comp.Survey 27-3, 326-327, 1995.
    • (1995) ACM Comp.Survey , vol.27 , Issue.3 , pp. 326-327
    • Diettrich, T.1
  • 3
    • 0003343155 scopus 로고
    • Discovery of Multiple-Level Association Rules from Large Databases
    • Han, J. and Fu, Y.: Discovery of Multiple-Level Association Rules from Large Databases, VLDB, 420-431, 1995.
    • (1995) VLDB , pp. 420-431
    • Han, J.1    Fu, Y.2
  • 4
    • 0342984487 scopus 로고
    • A Polynomial Approach to the Constructive Induction of Structural Knowledge
    • Kietz,J.U. and Morik, K.: A Polynomial Approach to the Constructive Induction of Structural Knowledge, Machine Learning 14, 193-217, 1994.
    • (1994) Machine Learning , vol.14 , pp. 193-217
    • Kietz, J.U.1    Morik, K.2
  • 5
    • 84874714593 scopus 로고    scopus 로고
    • Making Decision Trees More Accurate by Losing Information
    • FoIKS conf.
    • Miura, T., Shioya, I. and Mori, M.: Making Decision Trees More Accurate by Losing Information, FoIKS conf., LNCS-1762, 213-225, 2000.
    • (2000) LNCS , vol.1762 , pp. 213-225
    • Miura, T.1    Shioya, I.2    Mori, M.3
  • 6
    • 0024627518 scopus 로고
    • Inductive Decision Trees Using the Minimum Description Length Principle
    • Quinlan, J.R. and Rivest, R.L.: Inductive Decision Trees Using the Minimum Description Length Principle, Information and Computation, 80-3, 227-248, 1989.
    • (1989) Information and Computation , vol.80 , Issue.3 , pp. 227-248
    • Quinlan, J.R.1    Rivest, R.L.2
  • 8
    • 84949642734 scopus 로고    scopus 로고
    • Inductive Classification using Taxonomy
    • Shioya, I. and Miura, T. : Inductive Classification using Taxonomy, KRDB, 87-98, 2000.
    • (2000) KRDB , pp. 87-98
    • Shioya, I.1    Miura, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.