Abstract
The interest of introducing fuzzy predicates when learning rules is twofold. When dealing with numerical data, it enables us to avoid arbitrary discretization. Moreover, it enlarges the expressive power of what is learned by considering different types of fuzzy rules, which may describe gradual behaviors of related attributes or uncertainty pervading conclusions. This paper describes different types of first-order fuzzy rules and a method for learning each type. Finally, we discuss the interest of each type of rules on a benchmark example.
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Bouchon-Meunier, B., Marsala, C.: Learning fuzzy decision rules. In: Bezdek, J.C., Dubois, D., Prade, H. (eds.) Fuzzy Sets in Approximate Reasoning and Information Systems. The Handbooks of Fuzzy Sets Series, pp. 279–304. Kluwer Academic Publishers, Dordrecht (1999)
Delgado, M., Sanchez, D., Vila, M.A.: Fuzzy cardinality based evaluation of quantified sentences. Inter. J. of Approximate Reasoning 23, 23–66 (2000)
Dubois, D., Prade, H.: What are fuzzy rules and how to use them. Fuzzy Sets and Systems 84(2), 169–189 (1996)
Halpern, J.: An analysis of first-order logics of probability. Artificial Intelligence 46, 310–355 (1990)
Hüllermeier, E.: Implication-based fuzzy association rules. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 241–252. Springer, Heidelberg (2001)
Muggleton, S.H.: Inverse entailment and Progol. New Generation Computing 13, 245–286 (1995)
Muggleton, S.H.: Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence 5(041) (2000)
Nauck, D., Kruse, R.: Neuro-fuzzy methods in fuzzy rule generation. In: Bezdek, J.C., Dubois, D., Prade, H. (eds.) Fuzzy Sets in Approximate Reasoning and Information Systems. The Handbooks of Fuzzy Sets Series, pp. 305–334. Kluwer Acad. Pub, Dordrecht (1999)
Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)
Prade, H., Richard, G., Serrurier, M.: Learning first order fuzzy rules. In: De Baets, B., Kaynak, O., Bilgiç, T. (eds.) IFSA 2003. LNCS, vol. 2715, Springer, Heidelberg (2003)
Prade, H., Richard, G., Serrurier, M.: On the induction of different kinds of firstorder fuzzy rules. In: Nielsen, T.D., Zhang, N.L. (eds.) ECSQARU 2003. LNCS (LNAI), vol. 2711, pp. 370–381. Springer, Heidelberg (2003)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)
Quinlan, J.R.: Learning with continuous classes. In: Proc. Artificial Intelligence (AI 1992), Singapore, pp. 343–348 (1992)
Shibata, D., Inuzuka, N., Kato, S., Matsui, T., Itoh, H.: An induction algorithm based on fuzzy logic programming. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 268–274. Springer, Heidelberg (1999)
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Prade, H., Richard, G., Serrurier, M. (2003). Enriching Relational Learning with Fuzzy Predicates. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_36
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DOI: https://doi.org/10.1007/978-3-540-39804-2_36
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