Abstract
This paper elaborates on two techniques, deconstruction and composition, to handle complex data in order to learn from it. We propose typed higher-order logic as a suitable representation formalism for domains with complex structured data. Both techniques derive naturally from such framework. A naive sequential covering algorithm which uses both techniques is applied on well known learning datasets (simple and structured) to test them with good results. A further experiment on the change of knowledge representation is presented to showcase the robustness of our approach.
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Muggleton, S.H., Sternberg, M.J.E., Srinivasan, A., King, R.D.: Carcinogenesis predictions using ilp. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 273–287. Springer, Heidelberg (1997)
Giraud-Carrier, C., Bowers, A.F., Lloyd, J.W.: Classification of individuals with complex structure. In: Proceedings of the Seventeenth International Conference on Machine Learning (2000)
Kennedy, C., Lloyd, J.W., Bowers, A.F., Giraud-Carrier, C., MacKinney-Romero, R.: A framework for higher-order inductive machine learning. In: Proceedings of the COMPULOGNet Area Meeting on Representation Issues in Reasoning and Learning, pp. 19–25 (1997)
Bowers, A.F., Giraud-Carrier, C., Lloyd, J.W.: Classification of individuals with complex structure. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), pp. 81–88. Morgan Kaufmann, San Francisco (2000)
Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3, 261–283 (1989)
Kennedy, C., Giraud-Carrier, C.: An evolutionary approach to concept learning with structured data. In: Proceedings of the Fourth International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 331–336 (1999)
Lloyd, J.W.: Programming in an integrated functional and logic language. Journal of Functional and Logic Programming 1999(3) (1999)
MacKinney-Romero, R.: Learning using higher-order functions. In: Proceedings of ILP 1999 Late-Breaking Papers, pp. 42–46 (1999)
MacKinney-Romero, R., Giraud-Carrier, C.: Learning from highly-structured data by decomposition. In: Proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 436–441 (1999)
Michalski, R.S., Larson, J.B.: Inductive inference of VL decision rules. In: Workshop on Pattern-directed Inference Systems, pp. 33–44 (1977)
Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)
Giraud-Carrier, C., Flach, P.A., Lloyd, J.W.: Strongly-typed inductive concept learning. In: Proceedings of the Eighth International Conference on Inductive Logic Programming, pp. 185–194 (1998)
Srinivasan, A., King, R.D., Muggleton, S., Sternberg, M.: Structure-activity relationships derived by machine learning: The use of atoms and bonds and their connectivities to predict mutagenicity in inductive logic programming. Proceedings of the National Academy of Sciences 93, 438–442 (1996)
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© 2004 Springer-Verlag Berlin Heidelberg
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MacKinney-Romero, R., Giraud-Carrier, C. (2004). Inducing Classification Rules from Highly-Structured Data with Composition. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_27
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DOI: https://doi.org/10.1007/978-3-540-24694-7_27
Publisher Name: Springer, Berlin, Heidelberg
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