By Topic

Fuzzy Inductive Logic Programming: Learning Fuzzy Rules with their Implication

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
M. Serrurier ; IRIT, Univ. Paul Sabatier, Toulouse ; T. Sudkamp ; D. Dubois ; H. Prade

Inductive logic programming (ILP) is a generic tool aiming at learning rules from relational databases. Introducing fuzzy sets arid fuzzy implication connectives in this framework allows us to increase the expressive power of the induced rules while keeping the readability of the rules. Moreover, fuzzy sets facilitate the handling of numerical attributes by avoiding crisp and arbitrary transitions between classes. In this paper, the meaning of a fuzzy rule is encoded by its implication operator, which is to be determined in the learning process. An algorithm is proposed for inducing first order rules having fuzzy predicates, together with the most appropriate implication operator. The benefits of introducing fuzzy logic in ILP and the validation process of what has been learnt are discussed and illustrated on a benchmark

Published in:

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.

Date of Conference:

25-25 May 2005