Paper Info
Title
Improving Expressivity of Inductive Logic Programming by Learning Different Kinds of Fuzzy Rules
Abstract
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.
Year
DOI
Venue
2007
10.1007/s00500-006-0109-z
Soft Comput.
Keywords
Field
DocType
fuzzy rules,improving expressivity,inductive logic programming,inductive logic programming · fuzzy rules,appropriate confidence degree,classical rule,learning different kinds,classical foil inductive logic,confidence degree,programming algorithm,fuzzy predicate,different non-classical meaning,fuzzy rule,different purpose,expressive power
Inductive logic programming,Neuro-fuzzy,Defuzzification,Computer science,Inductive programming,Fuzzy logic,Theoretical computer science,Application domain,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Machine learning
Journal
Volume
Issue
ISSN
11
5
1433-7479
Citations 
PageRank 
References 
6
0.45
16
Authors
2
Name
Order
Citations
PageRank
Mathieu Serrurier126726.94
Henri Prade2105491445.02