Abstract—Fuzzy data is considered as an imprecise type of data with a source of uncertainty. Fuzzy numbers allow us to model uncertainty of data in an easy way which justifies the increasing interest on theoretical and practical aspects of fuzzy arithmetic. This paper presents a Fuzzy Bayesian Classifier (FBC) over LR-type fuzzy numbers with unknown conditional probability density function. A new version of K-NN method is used to estimate conditional probability density function for Bayesian classification of fuzzy numbers.Fairly good recognition rate has been obtained over fuzzy numbers in classification using FBC even in the presence of noise.
Index Terms—fuzzy data, Bayesian classifier, LR-type fuzzy numbers.
Hadi Sadoghi Yazdi is with the Computer Engineering Department, Ferdowsi University of Mashhad,
IRANMehri Sadoghi Yazdi is master student in Electrical and Computer Engineering Department, Shahid Beheshti University;
G. C. Tehran, IranAbedin Vahedian is with the Computer Engineering Department, Ferdowsi University of Mashhad, IRAN.
Cite: Hadi Sadoghi Yazdi, Mehri Sadoghi Yazdi and Abedin Vahedian, "Fuzzy Bayesian Classification of LR Fuzzy Numbers," International Journal of Engineering and Technology vol. 1, no. 5, pp. 415-423, 2009.
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