Improving expressivity of inductive logic programming by learning different kinds of fuzzy rules

作     者：Serrurier, Mathieu Prade, Henri 

作者机构：UPS IRIT F-31062 Toulouse 9 France 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2007年第11卷第5期

页      面：459-466页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：inductive logic programming fuzzy rules 

摘      要：Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability when learning classical rules or getting more expressivity by learning fuzzy rules. This latter concern is the topic of this paper. Indeed, introducing fuzzy predicates in the antecedent and in the consequent of rules may convey different non-classical meanings. The paper focuses on the learning of gradual and certainty rules, which have an increased expressive power and have no simple crisp counterpart. The benefit and the application domain of each kind of rules are discussed. Appropriate confidence degrees for each type of rules are introduced. These confidence degrees play a major role in the adaptation of the classical FOIL inductive logic programming algorithm to the induction of fuzzy rules for guiding the learning process. The method is illustrated on a benchmark example and a case-study database.