On decision boundaries of Naive Bayes in continuous domains

作者: Tapio Elomaa , Juho Rousu

DOI: 10.1007/978-3-540-39804-2_15

关键词:

摘要: Naive Bayesian classifiers assume the conditional independence of attribute values given class. Despite this in practice often violated assumption, these simple have been found efficient, effective, and robust to noise.

参考文章(29)
Hung-Ju Huang, Tzu-Tsung Wong, Why Discretization Works for Naive Bayesian Classifiers international conference on machine learning. pp. 399- 406 ,(2000)
Stephanie Sage, Pat Langley, Tractable Average-Case Analysis of Naive Bayesian Classifiers international conference on machine learning. pp. 220- 228 ,(1999)
David G. Stork, Richard O. Duda, Peter E. Hart, Pattern Classification (2nd ed.) ,(1999)
Geoffrey I. Webb, Ying Yang, Non-Disjoint Discretization for Naive-Bayes Classifiers international conference on machine learning. pp. 666- 673 ,(2002)
Bogdan S. Chlebus, Sinh Hoa Nguyen, On Finding Optimal Discretizations for Two Attributes Lecture Notes in Computer Science. pp. 537- 544 ,(1998) , 10.1007/3-540-69115-4_74
Keki B. Irani, Usama M. Fayyad, Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning international joint conference on artificial intelligence. ,vol. 2, pp. 1022- 1027 ,(1993)
Richard A Olshen, Charles J Stone, Leo Breiman, Jerome H Friedman, Classification and regression trees ,(1983)
J. Catlett, On changing continuous attributes into ordered discrete attributes Lecture Notes in Computer Science. pp. 164- 178 ,(1991) , 10.1007/BFB0017012
Mark A. Peot, Geometric implications of the naive Bayes assumption uncertainty in artificial intelligence. pp. 414- 419 ,(1996)