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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-24694-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
Online ISBN: 978-3-540-24694-7
eBook Packages: Springer Book Archive