Learning fuzzy concept definitions
- 30 December 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The symbolic approach to machine learning has developed algorithms for learning First Order Logic concept definitions. Nevertheless, most of them are limited because of their impossibility to cope with numeric features, typical of real-world data. In this paper, a method to face this problem is proposed. In particular, an extended version of the system ML-SMART is described, which is capable to automatically adjust the values of fuzzy sets used to define the semantics of the predicates in the concept description language. The learning strategy works in two separate phases: in the first one, the structure of the concept definition is learned by choosing tentative values for the fuzzy sets; in the second phase, the values are refined using a simple genetic algorithm, trying to get closer to an optimum assignment. The system is evaluated on a complex artificial domain, that shows the good potentialities of this approach.Keywords
This publication has 10 references indexed in Scilit:
- The utility of knowledge in inductive learningMachine Learning, 1992
- Classification in noisy environments using a distance measure between structural symbolic descriptionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Rigel: An inductive learning systemMachine Learning, 1991
- Learning logical definitions from relationsMachine Learning, 1990
- Automated concept acquisition in noisy environmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Quantified propositions in a linguistic logicInternational Journal of Man-Machine Studies, 1983
- A theory and methodology of inductive learningArtificial Intelligence, 1983
- A general concept of fuzzy connectives, negations and implications based on t-norms and t-conormsFuzzy Sets and Systems, 1983
- Pattern Recognition as Rule-Guided Inductive InferencePublished by Institute of Electrical and Electronics Engineers (IEEE) ,1980
- An interference matching technique for inducing abstractionsCommunications of the ACM, 1978