Constructing customized fuzzy sets using bayesian probability

Wooyong Chung, Jaehun Lee, Euntai Kim

Research output: Contribution to conferencePaper

Abstract

In the pattern classification problem, fuzzy classifier is useful when linguistic interpretability is required. The performance of a fuzzy classifier depends on its rules and membership functions. In this paper, we have proposed a method that constructs customized fuzzy sets by Bayesian probability. Based on the probability, we build probability ratio map for feature space and transformed it into membership functions. The proposed method can be used in a variety of applications such as a recommender system for web contents of IPTV services. Simulation and experiments results are given to show the better performance of the proposed method than other methods.

Original languageEnglish
Pages1376-1379
Number of pages4
Publication statusPublished - 2010
EventJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama, Japan
Duration: 2010 Dec 82010 Dec 12

Conference

ConferenceJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010
CountryJapan
CityOkayama
Period10/12/810/12/12

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

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    Chung, W., Lee, J., & Kim, E. (2010). Constructing customized fuzzy sets using bayesian probability. 1376-1379. Paper presented at Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010, Okayama, Japan.