A decomposition of classes via clustering to explain and improve Naive Bayes

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2837, Issue , 2003, Pages 444-455

  Vilalta, Ricardo ^a   Rish, Irina ^b  

^a University of Houston   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTER SCIENCE; DATABASE SYSTEMS; ESTIMATION; LEARNING SYSTEMS; PATTERN RECOGNITION; PROBABILITY; SPEECH RECOGNITION; ARTIFICIAL INTELLIGENCE; CLASSIFIERS; PROBABILITY DISTRIBUTIONS;

CLUSTERING; DOCUMENT CLASSIFICATION; LINEAR CLASSIFIERS; SUBSETS; CLASS DECOMPOSITION; CONDITIONAL PROBABILITIES; MULTIPLE REGIONS; NAIVE BAYES; NUMBER OF CLUSTERS; PROBABILITY ESTIMATE; PRODUCT DISTRIBUTIONS; REAL-WORLD DATASETS;

DATA MINING; CLUSTERING ALGORITHMS;

EID: 9444258129     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39857-8_40     Document Type: Conference Paper

References (15)

1. Blake C.L., Merz C.J.: UCI, Repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences (1998). http://www.ics.uci.edu/~mlearn/MLRepository.html.
2. Domingos P., Pazzani M.: On the Optimality of the Simple Bayesian Classifier Under Zero-One Loss. Machine Learning 29, pp. 103-130 (1997).
3. Duda R. O., Hart P. E., Stork D. G.: Pattern Classification. John Wiley Ed. 2nd Edition (2001).
4. Friedman N., Geiger D., Goldzmidt M.: Bayesian Network Classifiers. Machine Learning 29, pp. 131-163 (1997).
5. Garg A., Roth D.: Understanding Probabilistic Classifiers. European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, pp. 179-191 (2001).
6. Holte R.C., Acker L.E., Porter B.W.: Concept Learning and the Problem of Small Disjuncts. Eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 813-818 (1989).
7. Kohavi R., Becker B., Sommerfield D.: Improving Simple Bayes. European Conference on Machine Learning (1997).
8. Kohavi R.: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tee Hybrid. International Conference on Knowledge Discovery and Data Mining (1996).
9. Lewis P.M.: Approximating Probability Distributions to Reduce Storage Requirements. Information and Control, 2, pp. 214-225 (1959).
10. McLachlan G., Krishnan T.: The EM Algorithm and Extensions. John Wiley and Sons (1997).
11. Rish I., Hellerstein, J., Jayram, T.: An Analysis of Naive Bayes on Low-Entropy Distributions. IBM T.J. Watson Research Center, RC91994 (2001).
12. Webb G. I., Pazzani M. J.: Adjusted Probability Naive Bayes Induction. Tenth Australian Joint Conference on Artificial Intelligence. Springer-Verlag, pp. 285-295 (1998).
13. Witten I. H., Frank E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Academic Press, London U.K. (2000).
14. Zadrozny B., Elkan C.: Obtaining Calibrated Probability Estimates From Decision Trees and Naive Bayesian Classifiers. International Conference on Machine Learning (2001).
15. Zhang H., Ling C. X.: Geometric Properties of Naive Bayes in Nominal Domains. European Conference on Machine Learning, pp. 588-599 (2001).