Abstract
This paper presents a novel method for multi-relational classification via an aggregation-based Inductive Logic Programming (ILP) approach. We extend the classical ILP representation by aggregation of multiple-features which aid the classification process by allowing for the analysis of relationships and dependencies between different features. In order to efficiently learn rules of this rich format, we present a novel algorithm capable of performing aggregation with the use of virtual joins of the data. By using more expressive aggregation predicates than the existential quantifier used in standard ILP methods, we improve the accuracy of multi-relational classification. This claim is supported by experimental evaluation on three different real world datasets.
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Appice, A., Ceci, M., Lanza, A.: Discovery of spatial association rules in geo-referenced census data: a relational mining approach. Intelligent Data Analysis 7, 541–566 (2003)
Atramentov, A., Leiva, H., Honavar, V.: A multi-relational decision tree learning algorithm -implementation and experiments. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, Springer, Heidelberg (2003)
De Raedt, L., Lavrac, N.: Multiple Literal Learning in two Inductive Logic Programming Settings. Journal on Pure and Applied Logic (1996)
Dzeroski, S.: Multi-relational data mining: an introduction. SIGKDD Explorations 5(1) (2003)
Keim, D.A., Wawryniuk, M.: Identifying Most Predictive Items. In: Workshop on Pattern Representation and Management, Heraklion, Hellas (2004)
Knobbe, A.J., Siebes, A., Marseille, B.: Involving Aggregate Functions in Multi-Relational Search. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, Springer, Heidelberg (2002)
Lu, Q., Getoor, L.: Link-based Text Classification. In: Proceedings of ICML (2003)
Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: Proceedings of KDD (2003)
Perlich, C., Provost, F.: Aggregation-Based Feature Invention and Relational Concept Classes. In: Proceedings of KDD (2003)
Quinlan, J.R., Cameron-Jones, R.M.: FOIL: – A midterm report. In: Brazdil, P.B. (ed.) Machine Learning: ECML-93. LNCS, vol. 667, Springer, Heidelberg (1993)
Taskar, B., Segal, E., Koller, D.: Probabilistic classification and clustering in relational data. In: Proceedings IJCAI (2001)
Vens, C., Van Assche, A., Blockeel, H., Dzeroski, S.: First order random forests with complex aggregates. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, Springer, Heidelberg (2004)
Yin, X., Han, J., Yang, J., Yu, P.S.: CrossMine: Efficient Classification Across Multiple Database Relations. In: Proceedings of ICDE (2004)
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Frank, R., Moser, F., Ester, M. (2007). A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds) Knowledge Discovery in Databases: PKDD 2007. PKDD 2007. Lecture Notes in Computer Science(), vol 4702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74976-9_43
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DOI: https://doi.org/10.1007/978-3-540-74976-9_43
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