doi: 10.7763/IJET.2009.V1.78
Fuzzy Bayesian Classification of LR Fuzzy Numbers
- 1 Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- 2 Electrical and Computer Engineering Department, Shahid Beheshti University, Tehran, Iran
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.
Keywords
- fuzzy data
- Bayesian classifier
- LR-type fuzzy numbers
How to Cite
Hadi Sadoghi Yazdi, Abedin Vahedian, and Mehri Sadoghi Yazdi, "Fuzzy Bayesian Classification of LR Fuzzy Numbers," International Journal of Engineering and Technology, vol. 1, no. 5, pp. 415-423, 2009. https://doi.org/ 10.7763/IJET.2009.V1.78
Copyright & License
Copyright © 2009 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).