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Communication Dans Un Congrès Année : 2003

Enriching relational learning with fuzzy predicates

Résumé

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.

Dates et versions

hal-03373379 , version 1 (11-10-2021)

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Citer

Henri Prade, Gilles Richard, Mathieu Serrurier. Enriching relational learning with fuzzy predicates. 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2003), 2003, Cavtat-Dubrovnik, Croatia. pp.399-410, ⟨10.1007/978-3-540-39804-2_36⟩. ⟨hal-03373379⟩
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