سال انتشار: ۱۳۸۷

محل انتشار: دومین کنگره مشترک سیستم های فازی و سیستم های هوشمند

تعداد صفحات: ۷

نویسنده(ها):

Hadi Sadoghi Yazdi – Engineering Department, Tarbiat Moallem University of Sabzevar
Mehri Sadoghi Yazdi – Shahid Beheshti University of Tehran

چکیده:

Bayesian decision theory is a fundamental statistical approach to the problem of pattern classification. In this paper, we proposed a fuzzyBayesian classifier (FBC) over LR-type fuzzy numberswith unknown conditional probability density function. A new version of K-NN method is used to estimateconditional probability density function for Bayesian classification of fuzzy numbers. Experimentations show that this method has good recognition rate over fuzzy numbers even in presence of noise