Abstract
The new direction of the research in the field of data mining is the development of methods to handle imperfection (uncertainty, vagueness, imprecision,...). The main interest in this research is focused on probability models. Besides these there is an extensive study of the phenomena of imperfection in fuzzy logic. In this paper we concentrate especially on fuzzy logic programs (FLP) and Generalized Annotated Programs (GAP). The lack of the present research in the field of fuzzy inductive logic programming (FILP) is that every approach has its own formulation of the proof-theoretic part (often dealing with linguistic hedges) and lack sound and compete formulation of semantics. Our aim in this paper is to propose a formal model of FILP and induction of GAP programs (IGAP) based on sound and complete model of FLP (without linguistic hedges) and its equivalence with GAP. We focus on learning from entailment setting in this paper. We describe our approach to IGAP and show its consistency and equivalence to FILP. Our inductive method is used for detection of user preferences in a web search application. Finally, we compare our approach to several fuzzy ILP approaches.
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References
Bodenhofer, U., Drobics, M., Klement, E-P.: FS-FOIL: An Inductive learning method for extracting interpretable fuzzy descriptions. Int.J. Approximate Reasoning 32, 131–152 (2003)
Blockeel, H., Raedt, L.D., Jacobs, N.: Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3(1), 59–93 (1999)
Blockeel, H: Two Novel Methods for Learning Logic Prorams with Annotated Disjunctions. In: Proceedings of short papers on ILP 2006, Santiago De Compostela, Spain, UDC Press Service, pp. 31–33 (2006) ISBN:84-9749-206-4
Bratko, I., Šuc, D.: Learning qualitative models. AI Magazine 24, 107–119 (2003)
De Raedt, L., Kersting, K.: Probabilistic Inductive Logic Programming. In: Ben-David, S., Case, J., Maruoka, A. (eds.) ALT 2004. LNCS (LNAI), vol. 3244, pp. 19–36. Springer, Heidelberg (2004)
Džeroski, S., Lavrač, N.: An introduction to inductive logic programming. In: Džeroski, S., Lavrač, N. (eds.) Relational data mining, pp. 48–73. Springer, Heidelberg (2001)
Getoor, L., et al.: Learning probabilistic relational models. In: Džeroski, S., Lavrač, N. (eds.) Relational data mining, pp. 307–335. Springer, Heidelberg (2001)
Heckerman, D.: A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research (March 1995)
Horváth, T.: Unsupervised Learning of User Preferences by Ordinal Classification. In: Workshop NAZOU, Bystra Dolina, Slovakia, 2006: Vydavatelstvo STU. Bratislava, Slovakia, pp. 125–134 (2006) ISBN 80-227-2468-8
Horváth, T., Sudzina, F.: Measuring impact of information systems on business competitiveness using regression and ILP. In: Ekonomika firiem, Michalovce, Slovakia, 2004: PHF EU Bratislava, Košice, Slovakia, 2004, pp. 296–300 (2004) ISBN 80-225-1879-4
Horváth, T., Vojtáš, P.: GAP - Rule Discovery for Graded Classifciation. In: Fuernkranz, J. (eds.) Workshop of Advances in Inductive Rule Learning (W8) of ECML/PKDD 2004, Pisa, Italy, 2004: TU Darmstadt, Darmstadt, Germany, 2004, pp. 46–63 (2004)
Horváth, T., Vojtáš, P.: Fuzzy induction via generalized annotated programs. In: FDD 2004, Dortmund, Germany, 2004, pp. 419–433. Springer, Heidelberg (2005)
Kersting, K., De Raedt, L.: Bayesian Logic Programs, Technical Report 151, University of Freiburg, p. 52
Kifer, M., Subrahmanian, V.S.: Theory of generalized annotated logic programming and its applications. J. Logic Programming 12, 335–367 (1992)
Krajči, S., Lencses, R., Vojtáš, P.: A comparison of fuzzy and annotated logic programming. Fuzzy Sets and Systems 144, 173–192 (2004)
Lloyd, J.W.: Foundations of Logic Programming. Springer, Heidelberg (1987)
Prade, H., Richard, G., Serrurier, M.: Enriching relational Learning with fuzzy predicates. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 399–410. Springer, Heidelberg (2003)
Prade, H., Richard, G., Dubois, D., Sudkamp, T., Serrurier, M.: Learning first order fuzzy rules with their implication operator. In. Proc. of IPMU (2004)
Project NAZOU, http://nazou.fiit.stuba.sk/
Shibata, D., et al.: An induction algorithm based on fuzzy logic programming. In: Zhong, N., Zhou, L. (eds.) Methodologies for Knowledge Discovery and Data Mining. LNCS (LNAI), vol. 1574, pp. 268–273. Springer, Heidelberg (1999)
Vojtáš, P.: Fuzzy logic programming. Fuzzy Sets and Systems 124(3), 361–370 (2004)
Vojtáš, P., Vomlelová, M.: Transformation of deductive and inductive tasks between models of logic programming with imperfect information. In: Proceedings of IPMU, Editrice Universita La Sapienza, Roma, pp. 839–846 (2004)
Vojtáš, P., Vomlelová, M.: On models of comparison of multiple monotone classifications. In: Proc. IPMU 2006, Paris, France, Éditions EDK, Paris, pp. 1236–1243
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Horváth, T., Vojtáš, P. (2007). Induction of Fuzzy and Annotated Logic Programs. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_27
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