Landscapes of Naïve Bayes classifiers

Pattern Analysis and Applications

Volumn 11, Issue 1, 2008, Pages 59-72

  Hoare, Zoë ^a  

^a BANGOR UNIVERSITY   (United Kingdom)

Author keywords

Meta analysis; Na ve Bayes

Indexed keywords

EID: 37649020425     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-007-0079-5     Document Type: Article

References (55)

1. Aha D (1997) Lazy learning special issue editorial. Artif Intell Rev 11:7-10
2. Bressan M, Vitria J (2002) Improving Naive Bayes classification using class-conditional ICA. Lect Notes Artif Intell 2527:1-10
3. Cooper H, Hedges LV (eds) (1994) The handbook of research synthesis. Russell Sage Foundation, New York
4. Denton A, Perizo W (2004) A kernel-based semi-Naïve Bayesian classifier using P-Trees. In: Proceedings of the SIAM International Conference on Data Mining
5. Diao L, Hu K, Lu Y, Shi C (2002) A method to boost Naïve Bayesian classifiers. In: Lecture Notes in Computer Science, Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp 115-122
6. Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29:103-130
7. Duda RO, Hart PE, Stork DG (2001) Pattern classification and scene analysis, 2nd edn. Wiley-Interscience, New York
8. Freidman N, Geiger D, Goldschmidt M (1997) Bayesian network classifiers. Mach Learn 29(2):131-163
9. Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256-285
10. Freund Y, Schapire RE (1997) A decision theoretic genralization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119-139
11. Gama J (2003) Iterative Bayes. Theor Comput Sci 292:417-430
12. Huang H, Hsu C (2002) Bayesian classification for data from the same unknown class. IEEE Trans Syst Man Cybern B Cybern 32:137-145
13. Hunter JE, Schmidt FL (1990) Methods of meta-analysis. Sage publications, Newbury Park
14. Jain AK, Topchy A, Law MHC, Buhmann JM (2004) Landscape of clustering algorithms. In: Proceedings of the international conference on pattern recognition, ICPR, pp 260-263
15. James N, Liu K, Bavy N, Li L, Dillion T (2001) An improved Naïve Bayesian classifier technique coupled with a novel input solution method. IEEE Trans Syst Man Cybern C Appl Rev 31(2):249-256
16. Keogh E, Pazzani M (1999) Learning augmented Bayesian classifiers. A comparison of distribution-based and classification-based approaches. In: Proceedings of the international workshop on artificial intelligence and statistics, pp 225-230
17. Kleiner A, Sharp B (2000) A new algorithm for learning Bayesian classifiers. In: Proceedings of the 3rd IASTED international conference on artificial intelligence and soft computing, pp 191-197
18. Kohavi R (1996) Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, pp 202-207
19. Kohonen T (1989) Self-organization and associative memory. Springer, Berlin
20. Kononenko I (1991) Semi-Naïve Bayesian classifier. In: Proceedings of the 6th European Working Session on Learning, pp 206-219
21. Langley P, Sage S (1994) Induction of selective Bayesian classifiers. In: Proceedings of the 10th conference on UAI, pp 399-406
22. Lewis D (1998) Naïve Bayes (at forty): the independence assumption in information retrival. In: Proceedings of the 10th European conference on machine learning, pp 4-15
23. Lipsey M, Wilson DB (2001) Practical meta-analysis (applied social research methods). Sage Publications, London
24. Ma S, Shi H (2004) Tree augmented Naïve Bayes ensembles. In: Proceedings of the 3rd international conference on machine learning and cybernetics, pp 1497-1502
25. Manly B (1986) Multivariate statistical methods. a primer. Chapman and Hall, London
26. Meretakis D, Wüthrich B (1999) Extending Naïve Bayes classifiers using long itemsets. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, pp 165-174
27. Nürnberger A, Borgelt C, Klose A (1999) Improving Naïve Bayes classifiers using neuro-fuzzy learning. In: Proceedings of the 6th international conference on neural image processing, pp 154-159
28. Pazzani MJ (1996) Searching for dependencies in Bayesian classifiers. In: Proceedings of information, statistics and induction in science
29. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Fransisco
30. Phillips PJ, Newton EM (2002) Meta-analysis of face recognition algorithms. In: Proceedings of the 5th IEEE international conference on automatics face and gesture recognition, FGR'02, pp 235-241
31. Ratanamahatana C, Gunopulos D (2002) Scaling up the Naïve Bayesian classifier: using decision trees for feature selection. In: Proceedings of the Workshop on Data Cleaning and Pre-processing, ICDM'02
32. Ridgeway G, Madigan D, Richardson T, O'Kane J (1998) Interpretable boosted Naïve Bayes classification. In: Proceedings of the 4th international conference on knowledge discovery and data mining, pp 101-104
33. Robles V, Larranaga P, Menasalvas E, Perez MS, Herves V (2003) Improvement of Naïve Bayes collaborative filtering using interval estimation. In: Proceedings of the IEEE WIC international conference on web intelligence, pp 168-174
34. Rosell B, Hellerstein L (2004) Naïve Bayes with higher order attributes. In: Lecture Notes in Computer Science, Proceedings of the 17th Conference of the Canadian Society for Computational Studies of Intelligence, pp 105-119
35. Sahami M (1996) Learning limited dependence Bayesian classifiers. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, pp 334-338
36. Sammon JW (1969) A non-linear mapping for data structure analysis. IEEE Trans Comput 18:401-405
37. Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197-227
38. Schapire RE (1999) A brief introduction to boosting. In: Proceedings of the 16th international joint conference on artificial intelligence, pp 1401-1406
39. Scheiner SM, Gurevitch J (eds) (1993) Design and analysis of ecological experiments. Chapman and Hall, London
40. Schiffman S, Reynolds ML, Young FW (1981) Introduction to multidimensional scaling. Academic, London
41. Sohn SY (1999) Meta-analysis of classification algorithms for pattern recognition. IEEE Trans Pattern Anal Mach Intell 21(11):1137-1144
42. Störr HP (2002) A compact fuzzy extension of the Naïve Bayesian classification algorithm. In: Proceedings of InTech VJFuzzy 2002, pp 172-177
43. Ting K, Zheng Z (1999) Improving the performance of boosting for Naïve Bayesian classification. In: Proceedings of the 3rd Pacific-Asia conference on knowledge discovery and data mining, pp 296-305
44. Tsymbal A, Cunningham P, Pechenizkiy M, Puuronen S (2003) Search strategies for ensemble feature selection in medical diagnostics. Technical report, Trinity College Dublin, Ireland
45. Tsymbal A, Puuronen S (2002) Ensemble feature selection with the simple Bayesian classification in medical diagnostics. In: Proceedings of the 15th IEEE symposium on computer based medical systems, pp 225-230
46. Vilalta R, Rish I (2003) A decomposition of classes via clustering to explain and improve Naïve Bayes. In: Proceedings of the 14th European conference on machine learning
47. Wang L, Yuan S, Li H (2004) Boosting Naïve Bayes by active learning. In: Proceedings of the 3rd international conference on machine learning and cybernetics, pp 1383-1386
48. Wang Z, Webb GI (2002) Comparison of lazy Bayesian rule and tree-augmented Bayesian learning. In: Proceedings of IEEE international conference on data mining, pp 490-497
49. Webb G, Boughton J, Wang Z (2005) Not so Naïve Bayes: aggregating one dependence estimators. Mach Learn 58(1):5-24
50. Webb G, Pazzani MJ (1998) Adjusted probability Naïve Bayesian induction. In: Proceedings of the 11th Australian conference on artificial intelligence, pp 285-295
51. Wolf FM (1986) Meta-analysis: quantitative methods for research synthesis. Sage University Paper no. 59. Series on Quantitative Applications in the Social Sciences. Sage publications, London
52. Zhang H, Su J (2004) Conditional independence trees. Lect Notes Comput Sci 3201:513-524
53. Zheng Z, Webb GI (2000) Lazy learning of Bayesian rules. Mach Learn 41(1):53-84
54. Zhipeng X, Hsu W, Liu Z, Lee M (2002) SNNB: a selective neighbourhood based Naïve Bayes for lazy learning. In: Proceedings of advances in knowledge discovery and data mining. PAKDD, pp 104-114
55. Zheng Z (1998) Naïve Bayesian classifier committees. In: Proceedings of the European conference on machine learning, pp 196-207