Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data

Knowledge and Information Systems

Volumn 9, Issue 2, 2006, Pages 157-179

  Zhang, Jun ^a   Kang, Dae Ki ^a   Silvescu, Adrian ^a   Honavar, Vasant ^a  

^a Iowa State University   (United States)

Author keywords

Attribute value taxonomies; AVT based na ve Bayes learner; Partially specified data

Indexed keywords

TAXONOMIES;

ATTRIBUTE VALUE TAXONOMIES; ATTRIBUTE VALUES; BAYES CLASSIFIER; HIERARCHICAL GROUPINGS; LEARNING CLASSIFIERS; NATURAL GENERALIZATION; PARTIALLY SPECIFIED DATA; TRAINING EXAMPLE;

LEARNING ALGORITHMS;

EID: 33644505547     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-005-0211-z     Document Type: Article

References (46)

1. Almuallim H, Akiba Y, Kaneda S (1995) On handling tree-structured attributes. In: Proceedings of the twelfth international conference on machine learning. Morgan Kaufmann, pp 12-20
2. Almuallim H, Akiba Y, Kaneda S (1996) An efficient algorithm for finding optimal gain-ratio multiple-split tests on hierarchical attributes in decision tree learning. In: Proceedings of the thirteenth national conference on artificial intelligence and eighth innovative applications of artificial intelligence conference, vol 1. AAAI/MIT Press, pp 703-708
3. Aronis J, Provost F, Buchanan B (1996) Exploiting background knowledge in automated discovery. In: Proceedings of the second international conference on knowledge discovery and data mining. AAAI Press, pp 355-358
4. Aronis J, Provost F (1997) Increasing the efficiency of inductive learning with breadth-first marker propagation. In: Proceedings of the third international conference on knowledge discovery and data mining. AAAI Press, pp 119-122
5. Ashburner M, et al (2000) Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Gen 25:25-29
6. Bergadano F, Giordana A (1990) Guiding induction with domain theories. Machine learning - an artificial intelligence approach, vol. 3. Morgan Kaufmann, pp 474-492
7. Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am pp 35-43
8. Bhattacharya I, Getoor L (2004) Deduplication and group detection using links. KDD workshop on link analysis and group detection, Aug. 2004. Seattle
9. Caragea D, Silvescu A, Honavar V (2004) A framework for learning from distributed data using sufficient statistics and its application to learning decision trees. Int J Hybrid Intell Syst 1:80-89
10. Caragea D, Pathak J, Honavar V (2004) Learning classifiers from semantically heterogeneous data. In: Proceedings of the third international conference on ontologies, databases, and applications of semantics for large scale information systems. pp 963-980
11. Chen A, Chiu J, Tseng F (1996) Evaluating aggregate operations over imprecise data. IEEE Trans Knowl Data En 8:273-284
12. Clare A, King R (2001) Knowledge discovery in multi-label phenotype data. In: Proceedings of the fifth European conference on principles of data mining and knowledge discovery. Lecture notes in computer science, vol 2168. Springer, Berlin Heidelberg New York, pp 42-53
13. Cohen W (1996) Learning trees and rules with set-valued features. In: Proceedings of the thirteenth national conference on artificial intelligence. AAAI/MIT Press, pp 709-716
14. DeMichiel L (1989) Resolving database incompatibility: An approach to performing relational operations over mismatched domains. IEEE Trans Knowl Data Eng 1:485-493
15. Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc, Series B 39:1-38
16. desJardins M, Getoor L, Koller D (2000) Using feature hierarchies in Bayesian network learning. In: Proceedings of symposium on abstraction, reformulation, and approximation 2000. Lecture notes in artificial intelligence, vol 1864, Springer, Berlin Heidelberg New York, pp 260-270
17. Dhar V, Tuzhilin A (1993) Abstract-driven pattern discovery in databases. IEEE Trans Knowl Data Eng 5:926-938
18. Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29:103-130
19. Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131-163
20. Han J, Fu Y (1996) Attribute-oriented induction in data mining. Advances in knowledge discovery and data mining. AAAI/MIT Press, pp 399-421
21. Haussler D (1998) Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artif Intell 36:177-221
22. Hendler J, Stoffel K, Taylor M (1996) Advances in high performance knowledge representation. University of Maryland Institute for Advanced Computer Studies, Dept. of Computer Science, Univ. of Maryland, July 1996. CS-TR-3672 (Also cross-referenced as UMIACS-TR-96-56)
23. Kang D, Silvescu A, Zhang J, Honavar V (2004) Generation of attribute value taxonomies from data for data-driven construction of accurate and compact classifiers. In: Proceedings of the fourth IEEE international conference on data mining, pp 130-137
24. Kohavi R, Becker B, Sommerfield D (1997) Improving simple Bayes. Tech. Report, Data mining and visualization group, Silicon Graphics Inc.
25. Kohavi R, Provost P (2001) Applications of data mining to electronic commerce. Data Min Knowl Discov 5:5-10
26. Kohavi R, Mason L, Parekh R, Zheng Z (2004) Lessons and challenges from mining retail E-commerce data. Special Issue: Data mining lessons learned. Mach Learn 57:83-113
27. Koller D, Sahami M (1997) Hierarchically classifying documents using very few words. In: Proceedings of the fourteenth international conference on machine learning. Morgan Kaufmann, pp 170-178
28. Langley P, Iba W, Thompson K (1992) An analysis of Bayesian classifiers. In: Proceedings of the tenth national conference on artificial intelligence. AAAI/MIT Press, pp 223-228
29. McCallum A, Rosenfeld R, Mitchell T, Ng A (1998) Improving text classification by shrinkage in a hierarchy of classes. In: Proceedings of the fifteenth international conference on machine learning. Morgan Kaufmann, pp 359-367
30. McClean S, Scotney B, Shapcott M (2001) Aggregation of imprecise and uncertain information in databases. IEEE Trans Know Data Eng 13:902-912
31. Mitchell T (1997) Machine Learning. Addison-Wesley
32. Núñez M (1991) The use of background knowledge in decision tree induction. Mach Learn 6:231-250
33. Pazzani M, Kibler D (1992) The role of prior knowledge in inductive learning. Mach Learn 9:54-97
34. Pazzani M, Mani S, Shankle W (1997) Beyond concise and colorful: learning intelligible rules. In: Proceedings of the third international conference on knowledge discovery and data mining. AAAI Press, pp 235-238
35. Pereira F, Tishby N, Lee L (1993) Distributional clustering of English words. In: Proceedings of the thirty-first annual meeting of the association for computational linguistics. pp 183-190
36. Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, CA
37. Rissanen J (1978) Modeling by shortest data description. Automatica 14:37-38
38. Slonim N, Tishby N (2000) Document clustering using word clusters via the information bottleneck method. ACM SIGIR 2000. pp 208-215
39. Taylor M, Stoffel K, Hendler J (1997) Ontology-based induction of high level classification rules. SIGMOD data mining and knowledge discovery workshop, Tuscon, Arizona
40. Towell G, Shavlik J (1994) Knowledge-based artificial neural networks. Artif Intell 70:119-165
41. Undercoffer J, et al (2004) A target centric ontology for intrusion detection: Using DAML+OIL to classify intrusive behaviors. Knowledge Engineering Review - Special Issue on Ontologies for Distributed Systems, January 2004, Cambridge University Press
42. Walker A (1980) On retrieval from a small version of a large database. In: Proceedings of the sixth international conference on very large data bases. pp 47-54
43. Yamazaki T, Pazzani M, Merz C (1995) Learning hierarchies from ambiguous natural language data. In: Proceedings of the twelfth international conference on machine learning. Morgan Kaufmann, pp 575-583
44. Zhang J, Silvescu A, Honavar V (2002) Ontology-driven induction of decision trees at multiple levels of abstraction. In: Proceedings of symposium on abstraction, reformulation, and approximation 2002. Lecture notes in artificial intelligence, vol 2371. Springer, Berlin Heidelberg New York, pp 316-323
45. Zhang J, Honavar V (2003) Learning decision tree classifiers from attribute value taxonomies and partially specified data. In: Proceedings of the twentieth international conference on machine learning. AAAI Press, pp 880-887
46. Zhang J, Honavar V (2004) AVT-NBL: An algorithm for learning compact and accurate naive Bayes classifiers from attribute value taxonomies and data. In: Proceedings of the fourth IEEE international conference on data mining. IEEE Computer Society, pp 289-296