Fuzzy probabilistic rule induction

Author

van den Eijkel, G.C. ; Backer, E.

Author_Institution

Telematica Inst., Enschede, Netherlands

fYear

1999

fDate

36342

Firstpage

541

Lastpage

545

Abstract

The paper presents a novel uncertainty framework for rule induction from examples: a fuzzy probabilistic framework. The main motivation for this framework stems from the problem of data fit vs. mental fit in knowledge acquisition for decision support systems. The framework is based on an extension of the probability of a fuzzy event (as defined by L.A. Zadeh, 1968)), and is highly suitable for learning and reasoning with uncertainty. Experiments show that the framework results in a simple rule base by which highly accurate classifications are obtained and explained

Keywords

decision support systems; fuzzy set theory; inference mechanisms; knowledge acquisition; knowledge based systems; probability; uncertainty handling; accurate classifications; data fit; decision support systems; fuzzy event; fuzzy probabilistic framework; fuzzy probabilistic rule induction; knowledge acquisition; mental fit; reasoning with uncertainty; simple rule base; uncertainty framework; Algebra; Data analysis; Fuzzy logic; Information technology; Knowledge acquisition; Probability density function; Probability distribution; Random variables; State estimation; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American

Conference_Location

New York, NY

Print_ISBN

0-7803-5211-4

Type

conf

DOI

10.1109/NAFIPS.1999.781752

Filename

781752