The Naive Bayes Mystery: A classification detective story

Pattern Recognition Letters

Volumn 26, Issue 11, 2005, Pages 1752-1760

  Jamain, Adrien ^a   Hand, David J ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Comparative studies; Naive Bayes; Statlog; Supervised classification

Indexed keywords

DATA REDUCTION; DECISION THEORY; DIGITAL LIBRARIES; IMAGE ANALYSIS;

COMPARATIVE STUDIES; NAIVE BAYES; STATLOG; SUPERVIZED CLASSIFICATION;

PATTERN RECOGNITION;

EID: 19944378094     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.02.001     Document Type: Article

References (14)

1. O.K. Asparoukhov, and W.J. Krzanowski A comparison of discriminant procedures for binary variables Comput. Statist. Data Anal. 38 2001 139 160
2. Buntine, W., 1993. IND documentation, version 2.1. NASA Ames Research Center. Available from: 〈 http://ic.arc.nasa.gov/projects/bayes-group/ind/ IND-program.html 〉
3. B. Cestnik, I. Kononenko, and I. Bratko ASSISTANT 86: A knowledge-elicitation tool for sophisticated users Progress in Machine Learning: Proc. EWSL-87 1987 Sigma Press Bled, Yugoslavia 31 45
4. P. Clark, and T. Niblett Induction in noisy domains Proc. 2nd Eur. Work Session on Learning 1987 Sigma Press Glasgow, Scotland 11 30
5. T.G. Dietterich An experimental comparison of three methods for constructing ensembles of decisions trees: Bagging, boosting, and randomization Machine Learn. 40 2000 139 157
6. R.P.W. Duin A note on comparing classifiers Pattern Recognition Lett. 17 1996 529 536
7. D. Hand, and K. Yu Idiot's Bayes-not so stupid after all? Internat. Statist. Rev. 69 2001 385 398
8. Jamain, A., 2004. Meta-analysis of classification methods. Unpublished Ph.D. thesis, Department of Mathematics, Imperial College, London
9. P. Kontkanen, P. Myllymaki, T. Silander, and H. Tirri Bayes optimal instance-based learning 11th Eur. Conf. on Machine Learning 1998 Springer-Verlag Berlin 77 88
10. D. Michie, D.J. Spiegelhalter, and C.C. Taylor Machine learning, neural and statistical classification 1994 Ellis Horwood
11. S.L. Salzberg On comparing classifiers: Pitfalls to avoid and a recommended approach Data Mining Knowledge Discovery 1 1997 317 328
12. D.M. Titterington, G.D. Murray, L.S. Murray, D.J. Spiegelhalter, A.M. Skene, J.D.F. Habbema, and G.J. Gelpke Comparison of discrimination techniques applied to a complex data set of head injured patients J. Roy. Statist. Soc. Ser. A 144 1981 144 175
13. S.M. Weiss, and I. Kapouleas An empirical comparison of pattern recognition, neural nets, and machine learning classification methods IJCAI89: Proc. 11th Internat. Joint Conf. on Artificial Intelligence 1989 Morgan Kaufmann San Mateo, CA 781 787
14. Zarndt, F., 1995. A comprehensive case study: An examination of machine learning and connectionnist algorithms. Available from: 〈 http://citeseer.nj.nec.com/481595.html 〉