Increasing diagnostic accuracy by meta optimization of fuzzy rule bases

Mario Drobics, János Botzheim, L. Kóczy

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

3 Citations (Scopus)

Abstract

In medicine the decision on which test to choose for a given decision problem is a delicate problem. On the one hand a positive test should be a reliable indicator on the presence of a disease, while on the other hand a negative test is required to be an indicator on the absence of a disease. Of course, these two goals are conflicting and a balanced decision according to the current situation is required. Inductive learning methods for (fuzzy) rule bases are, however, typically not capable of optimizing such complex and problem depending goal functions. We therefore present a meta-learning algorithm which selects a subset from a previously generated set of fuzzy rules using bacterial evolutionary algorithms. We also present a study where the proposed method is used to generate a model for predicting the presence/absence of hepatitis, based on laboratory results.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
DOIs
Publication statusPublished - 2007
Event2007 IEEE International Conference on Fuzzy Systems, FUZZY - London, United Kingdom
Duration: Jul 23 2007Jul 26 2007

Other

Other2007 IEEE International Conference on Fuzzy Systems, FUZZY
CountryUnited Kingdom
CityLondon
Period7/23/077/26/07

Fingerprint

Fuzzy rules
Set theory
Evolutionary algorithms
Learning algorithms
Medicine

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety

Cite this

Drobics, M., Botzheim, J., & Kóczy, L. (2007). Increasing diagnostic accuracy by meta optimization of fuzzy rule bases. In IEEE International Conference on Fuzzy Systems [4295377] https://doi.org/10.1109/FUZZY.2007.4295377

Increasing diagnostic accuracy by meta optimization of fuzzy rule bases. / Drobics, Mario; Botzheim, János; Kóczy, L.

IEEE International Conference on Fuzzy Systems. 2007. 4295377.

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

Drobics, M, Botzheim, J & Kóczy, L 2007, Increasing diagnostic accuracy by meta optimization of fuzzy rule bases. in IEEE International Conference on Fuzzy Systems., 4295377, 2007 IEEE International Conference on Fuzzy Systems, FUZZY, London, United Kingdom, 7/23/07. https://doi.org/10.1109/FUZZY.2007.4295377
Drobics M, Botzheim J, Kóczy L. Increasing diagnostic accuracy by meta optimization of fuzzy rule bases. In IEEE International Conference on Fuzzy Systems. 2007. 4295377 https://doi.org/10.1109/FUZZY.2007.4295377
Drobics, Mario ; Botzheim, János ; Kóczy, L. / Increasing diagnostic accuracy by meta optimization of fuzzy rule bases. IEEE International Conference on Fuzzy Systems. 2007.
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