An empirical study of the use of relevance information in inductive logic programming

Journal of Machine Learning Research

Volumn 4, Issue 3, 2004, Pages 369-383

  Srinivasan, Ashwin ^a   King, Ross D ^b   Bain, Michael E ^c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b ABERYSTWYTH UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Expert assistance; ILP; Relevance of background predicates

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA HANDLING; DECISION MAKING; INFORMATION RETRIEVAL SYSTEMS; MATHEMATICAL MODELS; SYSTEM THEORY;

DOMAIN-SPECIFIC BACKGROUND INFORMATION; EXPERT-ASSISTANCE; INDUCTIVE LOGIC PROGRAMMING (ILP) SYSTEMS; RELEVANCE OF BACKGROUND PREDICATES;

LOGIC PROGRAMMING;

EID: 2342570988     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: 10.1162/153244304773633861     Document Type: Article

References (29)

1. J. Ashby and R.W. Tennant. Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP. Mutation Research, 257:229-306, 1991.
2. R. Benigni. (Q)SAR prediction of chemical carcinogenicity and the biological side of the structure activity relationship. In Proceedings of The Eighth International Workshop on QSARs in the Environmental Sciences, 1998. Held in Baltimore, May 16-20, 1998.
3. I. Bratko and M. Grobelnik. Inductive learning applied to program construction and verification. In Third International Workshop on Inductive Logic Programming, pages 279-292, 1993. Available as Technical Report IJS-DP-6707, J. Stefan Inst., Ljubljana, Slovenia.
4. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.
5. J. Cussens. Stochastic logic programs: Sampling, inference and applications. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000), pages 115-122, San Francisco, CA, 2000. Morgan Kaufmann.
6. A.K. Debnath, R.L. Lopez de Compadre, G. Debnath, A.J. Schusterman, and C. Hansch. Structure-Activity Relationship of Mutagenic Aromatic and Heteroaromatic Nitro compounds. Correlation with molecular orbital energies and hydrophobicity. Journal of Medicinal Chemistry, 34(2):786-797, 1991.
7. B. Dolsak and S. Muggleton. The application of Inductive Logic Programming to finite element mesh design. In S. Muggleton, editor, Inductive Logic Programming, pages 453-472. Academic Press, London, 1992.
8. S. Dzeroski, L. Dehaspe, B. Ruck, and W. Walley. Classification of river water quality data using machine learning. In Proceedings of the Fifth International Conference on the Development and Application of Computer Techniques Environmental Studies, 1994.
9. C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473-486. Academic Press, London, 1992.
10. D. Gabbay. Labelled deductive systems. Technical report, Centrum fur Informations und Spachver-arbeitung, Universitat Munchen, 1991.
11. R.D. King, A. Karwath, A. Clare, and L. Dehaspe. The Utility of Different Representations of Protein Sequence for Predicting Functional Class. Bioinformatics, 17:445-454, 2001.
12. R.D. King, S.H. Muggleton, A. Srinivasan, and M.J.E. Sternberg. Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc. of the National Academy of Sciences, 93: 438-442, 1996.
13. R.D. King, S.H. Muggleton, and M.J.E. Sternberg. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proc. of the National Academy of Sciences, 89(23): 11322-11326, 1992.
14. S. Muggleton. Inductive Logic Programming: derivations, successes and shortcomings. SIGART Bulletin, 5(1):5-11, 1994.
15. S. Muggleton. Inverse Entailment and Progol. New Gen. Comput., 13:245-286, 1995.
16. S. Muggleton. Stochastic logic programs. In L. DeRaedt, editor, Advances in Inductive Logic Programming, pages 254-264. IOS Press, 1996.
17. S. Muggleton. Semantics and derivation for stochastic logic programs. In UAI-2000 Workshop on Fusion of Domain Knowledge with Data for Decision Support, 2000.
18. S. Muggleton, R. King, and M. Sternberg. Predicting protein secondary structure using inductive logic programming. Protein Engineering, 5:647-657, 1992.
19. S.H. Muggleton, A. Srinivasan, R.D. King, and M.J.E. Sternberg. Biochemical knowledge discovery using Inductive Logic Programming. In H. Motoda, editor, First Conference on Discovery Science. Springer-Verlag, 1998. URL ftp://ftp.comlab.ox.ac.uk/pub/Packagea/ILP/Papers/AS/discovery98.ps.gz.
20. S. Nienhuys-Cheng and R. de Wolf. Foundations of Inductive Logic Programming. Springer, Berlin, 1997.
21. J. R. Quinlan. FOIL: a midterm report. In European Conference on Machine Learning. Springer-Verlag, Berlin, 1993.
22. A. Srinivasan. A study of two sampling methods for analysing large datasets with ILP. Data Mining and Knowledge Discovery, 3(1):95-123, 1999. URL ftp://ftp.comlab.ox.ac.uk/pub/Packages/ILP/Papers/AS/dami98b.ps.gz.
23. A. Srinivasan. Extracting context-sensitive models in Inductive Logic Programming. Machine Learning, 44:301-324, 2001. URL ftp://ftp.comlab.ox.ac.uk/ pub/Packages/ILP/Papers/AS/relev.ps.gz.
24. A. Srinivasan and R.D. King. Carcinogenesis predictions using ILP. In N. Lavrac and S. Dzeroski, editors, Proceedings of the Seventh International Workshop on Inductive Logic Programming (ILP97), volume 1297 of LNAI, pages 273-287, Berlin, 1997. Springer. URL ftp://ftp.comlab.ox.ac.uk/pub/Packages/ILP/ Papers/AS/ilp97.ps.gz. A version also in Intelligent Data Analysis in Medicine, Kluwer.
25. A. Srinivasan, R.D. King, and D.W. Bristol. An assessment of submissions made to the Predictive Toxicology Evaluation Challenge. In Proceedings of the Sixteenth International Conference on Artificial Intelligence (IJCAI-99). Morgan Kaufmann, Los Angeles, CA, 1999. URL ftp://ftp.comlab.ox.ac.uk/pub/Packages/ ILP/Papers/AS/ijcai99.ps.gz.
26. A. Srinivasan, R.D. King, S.H. Muggleton, and M.J.E. Sternberg. The Predictive Toxicology Evaluation Challenge. In Proceedings of the Fifteenth International Conference on Artificial Intelligence (IJCAI-97). Morgan Kaufmann, Los Angeles, CA, 1997. URL ftp://ftp.comlab.ox.ac.uk/pub/Packages/ILP/Papers/ AS/ijcai97.ps.gz.
27. A. Srinivasan, S.H. Muggleton, R.D. King, and M.J.E. Sternberg. Theories for mutagenicity: a study of first-order and feature based induction. Artificial Intelligence, 85:277-299, 1996. URL ftp://ftp.comlab.ox.ac.uk/pub/Packages/ILP/ Papers/AS/aij96.ps.gz.
28. S.M. Weiss and C.A. Kulikowski. Computer systems that learn. Morgan Kaufmann, San Mateo, CA, 1991.
29. J. Zelle and R. Mooney. Learning semantic grammars with constructive inductive logic programming. In Proceedings of the Eleventh National Conference on Artificial Intelligence, pages 817-822. Morgan Kaufmann, 1993.