A new design method for linguistically understandable fuzzy classifier

Heesung Lee, Sanghun Jang, Euntai Kim, Ho Gi Jung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many classification methods have been reported and the most popular ones among them are multilayer perceptron (MLP), nearest neighbor (NN), and support vector machine (SVM), etc. All of them have the weakness that they are not transparent or not clearly understandable to human beings. Sometimes, however, linguistically understandable classifiers could be preferred to the nontransparent models. Especially, when we are given a large set of data and we have to draw concise but interpretable hypothesis or conclusion, linguistically understandable classifiers should be required. In this paper, a linguistically understandable fuzzy classifier is presented and a new training method is proposed. To handle the uncertainties stemming from the problem or the measurement, the fuzzy classifier, the consequent part outputs the degree of truth for the assignment of each fuzzy set to the classes.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Fuzzy Systems - Proceedings
Pages447-450
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Conference on Fuzzy Systems - Jeju Island, Korea, Republic of
Duration: 2009 Aug 202009 Aug 24

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2009 IEEE International Conference on Fuzzy Systems
CountryKorea, Republic of
CityJeju Island
Period09/8/2009/8/24

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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    Lee, H., Jang, S., Kim, E., & Jung, H. G. (2009). A new design method for linguistically understandable fuzzy classifier. In 2009 IEEE International Conference on Fuzzy Systems - Proceedings (pp. 447-450). [5277120] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2009.5277120