Naive Bayesian classification of structured data

Machine Learning

Volumn 57, Issue 3, 2004, Pages 233-269

  Flach, Peter A ^a   Lachiche, Nicolas ^b  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b Faculté de Médecine   (France)

Author keywords

Bayesian classifier; First order features; Inductive logic programming; Knowledge representation; Structured data

Indexed keywords

BAYESIAN CLASSIFIERS; FIRST-ORDER FEATURES; LOGICAL CONSTRAINTS; PROBABILITY ESTIMATOR;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CONSTRAINT THEORY; DATA MINING; IMAGE PROCESSING; INFORMATION RETRIEVAL; KNOWLEDGE REPRESENTATION; LOGIC PROGRAMMING; MATHEMATICAL MODELS; MOLECULAR BIOLOGY; PREDICTIVE CONTROL SYSTEMS; PROBABILITY; SET THEORY;

DATA STRUCTURES;

EID: 4444320782     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:MACH.0000039778.69032.ab     Document Type: Conference Paper

References (46)

1. Badea, L., & Stanciu, M. (1999). Refinement operators can be (weakly) perfect. In Proceedings of the Ninth International Workshop on Inductive Logic Programming (pp. 21-32). Springer-Verlag.
2. Boström, H., & Asker, L. (1999). Combining divide-and-conquer and separate-and-conquer for efficient and effective rule induction. In Proceedings of the Ninth International Workshop on Inductive Logic Programming (pp. 33-43). Springer-Verlag.
3. M. Ceci, D.Malerba. Mr-SBC: a Multi-Relational Naive Bayes Classifier. : Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2838, pages 95-106, 2003.
4. Cussens, J. (2001). Parameter estimation in stochastic logic programs. Machine Learning, 43, 245-271.
5. Date, C. (1995). An introduction to database systems, Addison Wesley.
6. Dehaspe, L. (1997). Maximum entropy modeling with clausal constraints. In Proceedings of the Seventh International Workshop on Inductive Logic Programming (pp. 109-124). Springer-Verlag.
7. Dietterich, T., Lathrop, R., & Lozano-Perez, T. (1997). Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89, 31-71.
8. Dolšak, B., & Muggleton, S. (1992). The application of inductive logic programming to finite-element mesh design. In S. Muggleton (Ed.), Inductive logic programming. Academic Press.
9. Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130.
10. 13C NMR spectra with inductive logic programming. Applied Artificial Intelligence, 12, 363-383.
11. Džeroski, S., & Lavrač, N. (Eds.). (2001). Relational data mining. Springer:Verlag.
12. Fawcett, T. (2003). ROC graphs: Notes and practical considerations for data mining researchers. Technical report HPL-2003-4. HP Laboratories, Palo Alto, CA, USA. Available at http://www.hpl.hp.com/techreports/ 2003/HPL-2003-4.pdf.
13. Flach, P., Giraud-Carrier, C., & Lloyd, J. (1998). Strongly typed inductive concept learning. Proceedings of the Eighth International Conference on Inductive Logic Programming (pp. 185-194). Springer-Verlag.
14. Flach, P., & Lachiche, N. (1999). 1BC: A first-order Bayesian classifier. In Proceedings of the Ninth International Workshop on Inductive Logic Programming (pp. 92-103). Springer-Verlag.
15. Flach, P. (1999). Knowledge representation for inductive learning. Symbolic and Quantitative Approaches to Reasoning and Uncertainty (pp. 160-167). Springer-Verlag.
16. Flach, P., Gyftodimos, E., & Lachiche, N. (to appear). Probabilistic reasoning with terms. Linkoping Electronic Articles in Computer and Information Science. Available at http://www.ida.liu.se/ext/epa/cis/2002/011/tcover.html.
17. Flach, P., & Lachiche, N. (2001). Confirmation-guided discovery of first-order rules with tertius. Machine Learning, 42, 61-95.
18. Gärtner, T., Lloyd, J., & Flach, P. (2004). Kernels and distances for structured data. Machine Learning, 57:3, 205-232.
19. Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001). Learning probabilistic relational models. In S. Džeroski and N. Lavrač (Eds.), Relational data mining. Springer-Verlag.
20. Gyftodimos, E., & Flach, P. (2003). Hierarchical Bayesian networks: an approach to classification and learning for structured data. In Proceedings of the Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (pp. 12-21). Department of Informatics, University of Szeged.
21. Hand, D., & Till, R. (2001). A simple generalisation of the Area Under the ROC Curve for multiple class classification problems. Machine Learning, 45, 171-186.
22. John, G., & Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 338-345). Morgan Kaufmann.
23. Kersting, K., & De Raedt, L. (2000). Bayesian logic programs. In Proceedings of the Work-in-Progress Track at the Tenth International Conference on Inductive Logic Programming (pp. 138-155). CEUR Workshop Proceedings Series, Vol. 35.
24. Kersting, K., & De Raedt, L. (2001). Towards combining inductive logic programming with Bayesian networks. In Proceedings of the Eleventh International Conference on Inductive Logic Programming (pp. 118-131). Springer-Verlag.
25. Knobbe, A., de Haas, M., & Siebes, A. (2001). Propositionalisation and aggregates. In Proceedings of the Fifth European Conference on Principles of Data Mining and Knowledge Discovery (pp. 277-288). Springer-Verlag.
26. Kramer, S.,Lavrač, N.,&Flach, P. (2001). Propositionalization approaches to relational data mining. In S. Džeroski and N. Lavrač (Eds.), Relational data mining. Springer-Verlag.
27. Krogel, M.-A., & Wrobel, S. (2001). Transformation-based learning using multirelational aggregation. In Proceedings of the Eleventh International Conference on Inductive Logic Programming (pp. 142-155). Springer-Verlag.
28. Lachiche, N., & Flach, P. (2002). 1BC2: a true first-order Bayesian classifier. In Proceedings of the Twelfth International Conference on Inductive Logic Programming (pp. 133-148). Springer-Verlag.
29. Lachiche, N., & Flach, P. (2003). Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In Proceedings of the Twentieth International Conference on Machine Learning (pp. 416-423). AAAI Press.
30. Lavrač, N., & Flach, P. (2001). An extended transformation approach to inductive logic programming. ACM Transactions on Computational Logic, 2, 458-494.
31. Lavrač, N., Železný, F., & Flach, P. (2002). RSD: Relational subgroup discovery through first-order feature construction. In Proceedings of the Twelfth International Conference on Inductive Logic Programming (pp. 149-165). Springer-Verlag.
32. Lloyd, J. (1999). Programming in an integrated functional and logic language. Journal of Functional and Logic Programming, 1999.
33. Lloyd, J. (2003). Logic for learning: learning comprehensible theories from structured data. Springer-Verlag.
34. Lu, Q., & Getoor, L. (2003). Link-based classification. In Proceedings of the Twentieth International Conference on Machine Learning (pp. 496-503). AAAI Press.
35. Muggleton, S. (Ed.). (1992). Inductive logic programming. Academic Press.
36. Muggleton, S. (1995). Inverse entailment and Progol. New Generation Computing, 13, 245-286.
37. Muggleton, S. (1996). Stochastic logic programs. In L. De Raedt (Ed.), Advances in inductive logic programming. IOS Press.
38. Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20, 629-679.
39. Muggleton, S., Srinivasan, A., King, R., & Sternberg, M. (1998). Biochemical knowledge discovery using Inductive Logic Programming. In Proceedings of the first International Conference on Discovery Science (pp. 326-341). Springer-Verlag.
40. Pompe, U., & Kononenko, I. (1995). Naive Bayesian classifier within ILP-R. In Proceedings of the Fifth International Workshop on Inductive Logic Programming (pp. 417-436). Department of Computer Science, Katholieke Universiteit Leuven.
41. Rouveirol, C. (1994). Rattening and saturation: Two representation changes for generalization. Machine Learning, 14, 219-232.
42. Sato, T., & Kameya, Y (1997). Prism: A symbolic-statistical modeling language. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (pp. 1330-1335). Morgan Kaufmann.
43. Sato, T., & Kameya, Y. (2001). Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research, 15, 391-454.
44. Slattery, S., & Craven, M. (1998). Combining statistical and relational methods for learning in hypertext domains. In Proceedings of the Eighth International Conference on Inductive Logic Programming (pp. 38-52). Springer-Verlag.
45. Srinivasan, A., Muggleton, S., King, R., & Sternberg, M. (1994). Mutagenesis: ILP experiments in a non-determinate biological domain. In Proceedings of the Fourth International Workshop on Inductive Logic Programming (pp. 217-232). Gesellschaft für Mathematik und Datenverarbeitung MBH.
46. Taskar, B., Segal, E., & Koller, D. (2001). Probabilistic clustering in relational data. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 870-87). Morgan Kaufmann.