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Volumn 4212 LNAI, Issue , 2006, Pages 461-472

Improving the ranking performance of decision trees

Author keywords

AUC; Class probability; Decision tree; Ranking; Shrinkage; WPE

Indexed keywords

DATA STRUCTURES; LEARNING ALGORITHMS; PARAMETER ESTIMATION; PROBABILITY; STATISTICAL METHODS;

EID: 33750312217     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_44     Document Type: Conference Paper
Times cited : (8)

References (17)
  • 2
    • 0032645080 scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting and variants
    • Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Artificial Intelligence 36 (1989) 105-142
    • (1989) Artificial Intelligence , vol.36 , pp. 105-142
    • Bauer, E.1    Kohavi, R.2
  • 3
    • 0031191630 scopus 로고    scopus 로고
    • The use of the area under the ROC curve in the evaluation of machine learning algorithms
    • Bradley, A. P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (1997) 1145-1159
    • (1997) Pattern Recognition , vol.30 , pp. 1145-1159
    • Bradley, A.P.1
  • 6
    • 0003562954 scopus 로고    scopus 로고
    • A simple generalisation of the area under the ROC curve for multiple class classification problems
    • Hand, D. J., Till, R. J.: A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems. Machine Learning 45 (2001) 171-186
    • (2001) Machine Learning , vol.45 , pp. 171-186
    • Hand, D.J.1    Till, R.J.2
  • 12
    • 33750359725 scopus 로고    scopus 로고
    • Tree induction for probability-based ranking
    • Kluwer Academic Publishers
    • Provost, F., Domingos, P.: Tree Induction for Probability-based Ranking. Machine Learning. Kluwer Academic Publishers (2002)
    • (2002) Machine Learning
    • Provost, F.1    Domingos, P.2
  • 17
    • 0003259364 scopus 로고    scopus 로고
    • Obtaining calibrated probability estimates from decision trees and Naive Bayesian classifiers
    • Morgan Kaufmann
    • Zadrozny, B., Elkan, C.: Obtaining calibrated probability estimates from decision trees and Naive Bayesian classifiers. Proceedings of the 18th International Conference on Machine Learning. Morgan Kaufmann (2001) 609-616
    • (2001) Proceedings of the 18th International Conference on Machine Learning , pp. 609-616
    • Zadrozny, B.1    Elkan, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.