Abstract
Given domain-specific background knowledge and data in the form of examples, an Inductive Logic Programming (ILP) system extracts models in the data-analytic sense. We view the model-selection step facing an ILP system as a decision problem, the solution of which requires knowledge of the context in which the model is to be deployed. In this paper, "context" will be defined by the current specification of the prior class distribution and the client's preferences concerning errors of classification. Within this restricted setting, we consider the use of an ILP system in situations where: (a) contexts can change regularly. This can arise for example, from changes to class distributions or misclassification costs; and (b) the data are from observational studies. That is, they may not have been collected with any particular context in mind. Some repercussions of these are: (a) any one model may not be the optimal choice forall contexts; and (b) not all the background information provided may be relevant for all contexts. Using results from the analysis of Receiver Operating Characteristic curves, we investigate a technique that can equip an ILP system to reject those models that cannot possibly be optimal in any context. We present empirical results from using the technique to analyse two datasets concerned with the toxicity of chemicals (in particular, their mutagenic and carcinogenic properties). Clients can, and typically do, approach such datasets with quite different requirements. For example, a synthetic chemist would require models with a low rate of commission errors which could be used to direct efficiently the synthesis of new compounds. A toxicologist on the other hand, would prefer models with a low rate of omission errors. This would enable a more complete identification of toxic chemicals at a calculated cost of misidentification of non-toxic cases as toxic. The approach adopted here attempts to obtain a solution that contains models that are optimal for each such user according to the cost function that he or she wishes to apply. In doing so, it also provides one solution to the problem of how the relevance of background predicates is to be assessed in ILP.
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Ashby, J., & Tennant, R. W. (1991). Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP. Mutation Research, 257, 229–306.
Benigni, R. (1998). (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. Baltimore.
Bratko, I., & Grobelnik, M. (1993). Inductive learning applied to program construction and verification. In Third International Workshop on Inductive Logic Programming (pp. 279–292). Available as Technical Report IJSDP-6707, J. Stefan Inst., Ljubljana, Slovenia.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont: Wadsworth.
Cain, W. D. (1969). Engineering product design. London: London Business Books.
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Schusterman, A. J., & Hansch, C. (1991). 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.
Dolsak, B., & Muggleton, S. (1992). The application of inductive logic programming to finite element mesh design. In S. Muggleton (Ed.), Inductive logic programming. London: Academic Press.
Dzeroski, S., Dehaspe, L., Ruck, B., & Walley, W. (1994). 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. Southampton: Computational Mechanics Publications.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996). Advances in knowledge discovery and data mining. Menlo Park, CA: AAAI press (co-published by MIT Press).
Feng, C. (1992). Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton (Ed.). Inductive logic programming. London: Academic Press.
Hand, D. J. (1997). Construction and assessment of classification rules. Chichester: Wiley.
King, R. D., Muggleton, S. H., Srinivasan, A., & Sternberg, M. J. E. (1996). Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. In Proc. of the National Academy of Sciences, 93, 438–442.
King, R. D., Muggleton, S. H., & Sternberg, M. J. E. (1992). Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. In Proc. of the National Academy of Sciences, 89:23, 11322–11326.
Michie, D. (1990). Personal models of rationality. Journal of Statistical Planning and Inference, 25, 381–399.
Muggleton, S., King, R., & Sternberg, M. (1992). Predicting protein secondary structure using inductive logic programming. Protein Engineering, 5, 647–657.
Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19:20, 629–679.
Nienhuys-Cheng, S., & de Wolf, R. (1997). Foundations of inductive logic programming. Berlin: Springer.
Norusis, M. J. (1994). SPSS: Base system user guide. release 6.0. 444 N Michigan Ave, Chicago, Illinois 60611: SPSS Inc.
O'Hagan, A. (1999). Kendall's advanced theory of statistics (Vol. 2B). London: Arnold.
Provost, F., & Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97) (pp. 43–48). Menlo Park, CA: AAAI Press.
Provost, F., & Fawcett, T. (1998). Robust classification systems for imprecise environments. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98). Menlo Park, CA: AAAI Press.
Provost, F., & Fawcett, T. (2000). Robust classification for imprecise environments. Machine Learning, (to appear). Version available at http://www.stern.nyu.edu/?fprovost.
Quinlan, J. R. (1993). FOIL: a midterm report. In European Conference on Machine Learning. (Vol. 667, LNAI, pp. 3–20). Berlin: Springer-Verlag.
Salzberg, S. L. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1, 317–327.
Scott, M. J. J., Niranjan, M., & Prager, R. W. (1998). Parcel: Feature subset selection in variable cost domains. Technical Report CUED/F-INFENG/TR.323, Cambridge University Engineering Department, Cambridge, UK. Version available at http://svr-www.eng.cam.ac.uk/reports/people/niranjan.html.
Srinivasan, A. (1999). Note on the location of optimal classifiers in n-dimensional ROC space. Technical Report PRG-TR-2-99, Oxford University Computing Laboratory, Oxford.
Srinivasan, A. (2000). A study of two probabilistic methods for searching large spaces with ILP. (Submitted).
Srinivasan, A., & Camacho, R. C. (1999). Numerical reasoning with an ILP program capable of lazy evaluation and customised search. Journal of Logic Programming, 40:2, 3, 185–214.
Srinivasan, A., & King, R. D. (1997). Carcinogenesis predictions using ILP. In N. Lavrac, & S. Dzeroski (Ed.) In Proceedings of the Seventh International Workshop on Inductive Logic Programming (ILP97) LNAI (Vol. 1297). Berlin: Springer. A version also in Intelligent Data Analysis in Medicine, Kluwer.
Srinivasan, A., King, R. D., & Bristol, D. W. (1999). An assessment of ILP-assisted models for toxicity and the PTE-3 experiment. In S. Dzeroski, & P. A. Flach (Ed.). In Proceedings of the Ninth International Workshop on Inductive Logic Programming (ILP99) LNAI (Vol. 1634). Berlin: Springer.
Srinivasan, A., King, R. D., & Bristol., D. W. (1999). An assessment of submissions made to the predictive toxicology evaluation challenge. In Proceedings of the Sixteenth International Conference on Artificial Intelligence (IJCAI-99). Los Angeles, CA: Morgan Kaufmann.
Srinivasan, A., King, R. D., Muggleton, S. H., & Sternberg, M. J. E. (1997) The predictive toxicology evaluation challenge. In Proceedings of the Fifteenth International Conference on Artificial Intelligence (IJCAI-97). Los Angeles, CA: Morgan Kaufmann.
Srinivasan, A., Muggleton, S. H., King, R. D., & Sternberg, M. J. E. (1999) Theories for mutagenicity: A study of first-order and feature based induction. Artificial Intelligence, 85, 277–299.
Stuart, A., Ord, K., & Arnold, S. (1999). Kendall's advanced theory of statistics (Vol. 2A). London: Arnold.
Walpole, R. E., & Myers, R. H. (1999). Probability and statistics for engineers and scientists (2nd Ed.). New York: Collier Macmillan.
Weiss, S. M., & Kulikowski, C. A. (1991). Computer systems that learn. San Mateo, CA: Morgan Kaufmann.
Zelle, J., & Mooney, R. (1993). Learning semantic grammars with constructive inductive logic programming. In Proceedings of the Eleventh National Conference on Artificial Intelligence (pp. 817–822). Menlo Park, CA: AAAI Press.
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Srinivasan, A. Extracting Context-Sensitive Models in Inductive Logic Programming. Machine Learning 44, 301–324 (2001). https://doi.org/10.1023/A:1010980106294
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DOI: https://doi.org/10.1023/A:1010980106294