Application of uncertainty measures on credal sets on the naive Bayesian classifier

References

• Abellán , J. 2006 . Uncertainty measures on probability intervals from imprecise Dirichlet model . Int. J. Gen. Syst. , 35 ( 3 ) To appear in
   Google Scholar
• Abellán , J. and Moral , S. 2000 . A non-specificity measure for convex sets of probability distributions . Int. J. Unc. Fuzz. Knowl. Based Syst. , 8 : 357 – 367 .
   Google Scholar
• Abellán , J. and Moral , S. 2003 . Maximum of entropy for credal sets . Int. J. Unc. Fuzz. Knowl. Based Syst. , 11 ( 5 ) : 587 – 597 .
   Web of Science ®Google Scholar
• Abellán , J. and Moral , S. 2005a . Maximum difference of entropies as a non-specificty measure for credal sets . Int. J. Gen. Syst. , 34 ( 3 ) : 201 – 214 .
   Web of Science ®Google Scholar
• Abellán , J. and Moral , S. 2005b . Upper entropy of credal sets. Applications to credal classification . Int. J. Approx. Reasoning , 39 : 235 – 255 .
   Web of Science ®Google Scholar
• Abellán , J. and Moral , S. 2006 . An algorithm that computes the upper entropy for order-2 capacities . Int. J. Unc. Fuzz. Knowl. Based Syst. , 14 ( 2 ) : 141 – 154 .
   Google Scholar
• Abellán , J. , Klir , G.J. and Moral , S. 2006a . Disaggregated total uncertainty measure for credal sets . Int. J. Gen. Syst. , 35 ( 1 ) : 29 – 44 .
   Web of Science ®Google Scholar
• Abellán , J. , Moral , S. , Gómez , M. and Masegosa , A. 2006b . “ Varying parameter in classification based on imprecise probabilities ” . In Third international conference on Soft Methods in Probability and Statistics 2006 Accepted in
   Google Scholar
• Bernard , J.M. 2005 . An introduction to the imprecise Dirichlet model for multinomial data . Int. J. Approx. Reasoning , 39 : 123 – 150 .
   Web of Science ®Google Scholar
• Campos , L.M. , de Huete , J.F and Moral , S. 1994 . Probability intervals: a tool for uncertainty reasoning . Int. J. Unc. Fuzz. Knowl. Based Syst. , 2 : 167 – 196 .
   Google Scholar
• Dempster , A.P. 1967 . Upper and lower probabilities induced by a multivaluated mapping . Ann. Math. Statist. , 38 : 325 – 339 .
   Google Scholar
• Duda , R.O. and Hart , P.E. 1973 . Pattern Classification And Scene Analysis , New York : John Wiley and Sons .
   Google Scholar
• Friedman , N. , Geiger , D. and Goldszmidt , M. 1997 . Bayesian networks classifiers . Mach. Learn. , 29 : 131 – 163 .
   Web of Science ®Google Scholar
• Huang , K. , King , I. and Lyu , M. 2002 . “ Learning maximum likelihood semi-naive Bayesian network classifier ” . In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics Vol. 3 , Hammamet, Tunisia
   Google Scholar
• Keogh , E.J. and Pazzani , M.J. 1999 . “ Learning augmented Bayesian classifiers: a comparizon of distribution-based and classification-based approaches ” . In Proc. Int. Workshop on Artificial Intelligence and Statistics 225 – 230 .
   Google Scholar
• Kittler , J. 1986 . “ Feature selection and extraction ” . In Handbook of Pattern Recognition and Image Processing , Edited by: Young , T.Y. and Fu , K.S. New York : Academic Press .
   Google Scholar
• Klir , G.J. 2006 . Uncertainty and Information: Foundations of Generalize Information theory , Hoboken, NJ : John Wiley .
   Google Scholar
• Klir , G.J. and Smith , R.M. 2001 . On measuring uncertainty and uncertainty-based information: recent developments . Ann. Math. Art. Intell. , 32 : 5 – 33 .
   Web of Science ®Google Scholar
• Klir , G.J. and Wierman , M.J. 1998 . Uncertainty-Based Information , Heidelberg, Germany : Phisica-Verlag .
   Google Scholar
• Kononenko , I. 1991 . “ Semi-naive Bayesian classifier ” . In Proceedings of the 6th European Working Session on Machine Learning , 206 – 219 . Berlin : Springer-Verlag .
   Google Scholar
• Kononenko , I. and Zupan , B. 1999 . “ Attribute mining: evaluation, discretization, subset selection and constructive induction ” . In Proceedings of the ICML-99 Workshop on From Machine Learning to Knowledge Discovery in Databases 1 – 15 . Bled, Slovenia
   Google Scholar
• Langley , P. and Sage , S. 1994 . “ Induction of selective Bayesian classifiers ” . In Proceedings of the Tenth Conference Uncertainty in Artificial Intelligence , 399 – 406 . San Francisco : Morgan Kaufmann .
   Google Scholar
• Pazzani , M.J. 1996 . Constructive induction of Cartesian product attribute . Inform. Stat. Induction Sci. , : 66 – 77 .
   Google Scholar
• Quinlan , J.R. 1986 . Induction of decision trees . Mach. Learn. , 1 : 81 – 106 .
   Google Scholar
• Quinlan , J.R. 1993 . Programs for Machine Learning , San Francisco : Morgan Kaufmann . series in Machine Learning
   Google Scholar
• Shafer , G. 1976 . A Mathematical Theory of Evidence , Princeton : Princeton University Press .
   Google Scholar
• Shannon , C.E. 1948 . A mathematical theory of communication . Bell Syst. Tech. J. , 27 : 379 – 423 . 623–656
   Web of Science ®Google Scholar
• Walley , P. 1991 . Statistical Reasoning with Imprecise Probabilities , New York : Chapman and Hall .
   Google Scholar
• Walley , P. 1996 . Inferences from multinomial data: learning about a bag of marbles . J. Roy. Statist. Soc. B , 58 : 3 – 57 .
   Web of Science ®Google Scholar
• Webb , G.I. , Boughton , J. and Wang , Z. 2005 . Not so naive Bayes: aggregating one-dependence estimators . Mach. Learn. , 58 : 5 – 24 .
   Web of Science ®Google Scholar
• Zheng , Z. and Webb , G.I. 2000 . Lazy learning of Bayesian rules . Mach. Learn. , 41 : 53 – 84 .
   Web of Science ®Google Scholar
• Zheng , Z. and Webb , G.I. 2005 . “ A comparative study of semi-naive Bayes methods in classification learning ” . In Proc. Fourth Australasian Conference on Knowledge Discovery and Data Mining 141 – 156 .
   Google Scholar