Naïve bayes ensembles with a random oracle

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4472 LNCS, Issue , 2007, Pages 450-458

  Rodríguez, Juan J ^a   Kuncheva, Ludmila I ^b  

^a UNIVERSITY OF BURGOS   (Spain)

^b BANGOR UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); DECISION TREES; PROBLEM SOLVING; RANDOM PROCESSES;

BAYES ENSEMBLES; MULTIBOOST; NAIVE BAYES CLASSIFIERS (NB); RANDOM HYPERPLANES; RANDOM-ORACLE DECISION TREES;

COMPUTER PROGRAMMING LANGUAGES;

EID: 37249010079     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72523-7_45     Document Type: Conference Paper

References (16)

1. E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36( 1-2): 105-139, 1999.
2. R.R. Bouckaert. Naive Bayes classifiers that perform well with continuous variables. In 17th Australina Conference on AI (AI 04), Lecture Notes in AI. Springer, 2004.
3. L. Breiman. Bagging predictors. Technical Report 421, Department of Statistics, University of California, Berkeley, 1994.
4. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
5. J. Demšar. Statistical comparison of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, 2006.
6. T.G. Dietterich. Ensemble methods in machine learning. In Multiple Classifier Systems 2000, pages 1-15, 2000.
7. C.L. Blake D.J. Newman, S. Hettich and C.J. Merz. UCI repository of machine learning databases, 1998.
8. P. Domingos and M. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29:103-130, 1997.
9. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
10. D.J. Hand and K. Yu. Idiot's bayes -not so stupid after all? International Statistical Review, 69:385-399, 2001.
11. T. K. Ho. The random space method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844, 1998.
12. L. I. Kuncheva and J. J. Rodriguez. Classifier ensembles with a random linear oracle. IEEE Transactions on Knowledge and Data Engineering, 19(4):500-508, 2007.
13. L.I. Kuncheva. On the optimality of naïve bayes with dependent binary features. Pattern Recognition Letters, 27:830-837, 2006.
14. P. Melville and R. J. Mooney. Creating diversity in ensembles using artificial data. Information Fusion, 6(1):99-111, 2005.
15. G. I. Webb. Multiboosting: A technique for combining boosting and wagging. Machine Learning, 40(2): 159-196, 2000.
16. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2nd edition, 2005. http://www.cs.waikato.ac.nz/ml/weka.