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Volumn 26, Issue , 2012, Pages 239-245

Improving Tree augmented Naive Bayes for class probability estimation

Author keywords

Class probability estimation; Conditional log likelihood; Ensemble learning; Naive Bayes; Tree Augmented Naive Bayes

Indexed keywords

CLASS PROBABILITIES; ENSEMBLE LEARNING; LOG LIKELIHOOD; NAIVE BAYES; TREE AUGMENTED NAIVE BAYES;

EID: 84155186550     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2011.08.010     Document Type: Article
Times cited : (120)

References (27)
  • 1
    • 1242285091 scopus 로고    scopus 로고
    • Active sampling for class probability estimation and ranking
    • M. Saar-Tsechansky, and F. Provost Active sampling for class probability estimation and ranking Machine Learning 54 2 2004 153 178
    • (2004) Machine Learning , vol.54 , Issue.2 , pp. 153-178
    • Saar-Tsechansky, M.1    Provost, F.2
  • 3
    • 34250798744 scopus 로고    scopus 로고
    • Developing mining-grid centric E-finance portals for risk management and decision making
    • DOI 10.1142/S0218001407005594, PII S0218001407005594
    • J. Hu, N. Zhong, and Y. Shi Developing mining-grid centric E-finance portals for risk management and decision making International Journal of Pattern Recognition and Artificial Intelligence 21 4 2007 639 658 (Pubitemid 46976171)
    • (2007) International Journal of Pattern Recognition and Artificial Intelligence , vol.21 , Issue.4 , pp. 639-658
    • Hu, J.1    Zhong, N.2    Shi, Y.3
  • 4
    • 0042346121 scopus 로고    scopus 로고
    • Tree induction for probability-based ranking
    • F.J. Provost, and P. Domingos Tree induction for probability-based ranking Machine Learning 52 3 2003 199 215
    • (2003) Machine Learning , vol.52 , Issue.3 , pp. 199-215
    • Provost, F.J.1    Domingos, P.2
  • 8
    • 14344256569 scopus 로고    scopus 로고
    • Learning Bayesian Network classifiers by maximizing conditional likelihood
    • Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
    • D. Grossman, P. Domingos, Learning Bayesian network classifiers by maximizing conditional likelihood, in: Proceedings of the Twenty-First International Conference on Machine Learning, ICML, ACM Press, 2004, pp. 361-368. (Pubitemid 40290829)
    • (2004) Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 , pp. 361-368
    • Grossman, D.1    Domingos, P.2
  • 11
  • 12
    • 69949110656 scopus 로고    scopus 로고
    • On the classification performance of TAN and general Bayesian networks
    • M.G. Madden On the classification performance of TAN and general Bayesian networks Knowledge-Based Systems 22 7 2009 489 495
    • (2009) Knowledge-Based Systems , vol.22 , Issue.7 , pp. 489-495
    • Madden, M.G.1
  • 17
    • 67650498428 scopus 로고    scopus 로고
    • Structure identification of Bayesian classifiers based on GMDH
    • J. Xiao, C. He, and X. Jiang Structure identification of Bayesian classifiers based on GMDH Knowledge-Based Systems 22 6 2009 461 470
    • (2009) Knowledge-Based Systems , vol.22 , Issue.6 , pp. 461-470
    • Xiao, J.1    He, C.2    Jiang, X.3
  • 21
    • 0031191630 scopus 로고    scopus 로고
    • The use of the area under the ROC curve in the evaluation of machine learning algorithms
    • PII S0031320396001422
    • A.P. Bradley The use of the area under the ROC curve in the evaluation of machine learning algorithms Pattern Recognition 30 1997 1145 1159 (Pubitemid 127406521)
    • (1997) Pattern Recognition , vol.30 , Issue.7 , pp. 1145-1159
    • Bradley, A.P.1
  • 22
    • 0003562954 scopus 로고    scopus 로고
    • A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
    • DOI 10.1023/A:1010920819831
    • D.J. Hand, and R.J. Till A simple generalisation of the area under the ROC curve for multiple class classification problems Machine Learning 45 2 2001 171 186 (Pubitemid 33635984)
    • (2001) Machine Learning , vol.45 , Issue.2 , pp. 171-186
    • Hand, D.J.1    Till, R.J.2
  • 23
    • 14844351034 scopus 로고    scopus 로고
    • Not so naive Bayes: Aggregating one-dependence estimators
    • DOI 10.1007/s10994-005-4258-6
    • G.I. Webb, J. Boughton, and Z. Wang Not so Naive Bayes: aggregating one-dependence estimators Machine Learning 58 2005 5 24 (Pubitemid 40356736)
    • (2005) Machine Learning , vol.58 , Issue.1 , pp. 5-24
    • Webb, G.I.1    Boughton, J.R.2    Wang, Z.3
  • 26
    • 0003957032 scopus 로고    scopus 로고
    • Data Mining: Practical machine learning tools and techniques
    • Morgan Kaufmann San Francisco
    • I.H. Witten, and E. Frank Data Mining: Practical machine learning tools and techniques second ed. 2005 Morgan Kaufmann San Francisco
    • (2005) Second Ed.
    • Witten, I.H.1    Frank, E.2
  • 27
    • 0042847140 scopus 로고    scopus 로고
    • Inference for the generalization error
    • C. Nadeau, and Y. Bengio Inference for the generalization error Machine Learning 52 2003 239 281
    • (2003) Machine Learning , vol.52 , pp. 239-281
    • Nadeau, C.1    Bengio, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.