Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs

Daniela Girimonte, Robert Babuška, J. Abonyi

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

5 Citations (Scopus)

Abstract

A method for selecting regressors in nonlinear models with mixed discrete (categorical) and continuous inputs is proposed. Given a set of input-output data and an initial superset of potential inputs, the relevant inputs are selected by a model-free search algorithm. Fuzzy clustering is used to quantize continuous data into subsets that can be handled in a similar way as discrete data. Two simulation examples and one real-world data set are included to illustrate the performance of the proposed method and compare it with the performance of regression trees. For small to medium size problems (up to 15 candidate inputs), the proposed method works effectively. For larger problems, the computational load becomes too high.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Pages383-387
Number of pages5
Volume1
DOIs
Publication statusPublished - 2004
Event2004 IEEE International Conference on Fuzzy Systems - Proceedings - Budapest, Hungary
Duration: Jul 25 2004Jul 29 2004

Other

Other2004 IEEE International Conference on Fuzzy Systems - Proceedings
CountryHungary
CityBudapest
Period7/25/047/29/04

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Fuzzy clustering

ASJC Scopus subject areas

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

Cite this

Girimonte, D., Babuška, R., & Abonyi, J. (2004). Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs. In IEEE International Conference on Fuzzy Systems (Vol. 1, pp. 383-387) https://doi.org/10.1109/FUZZY.2004.1375754

Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs. / Girimonte, Daniela; Babuška, Robert; Abonyi, J.

IEEE International Conference on Fuzzy Systems. Vol. 1 2004. p. 383-387.

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

Girimonte, D, Babuška, R & Abonyi, J 2004, Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs. in IEEE International Conference on Fuzzy Systems. vol. 1, pp. 383-387, 2004 IEEE International Conference on Fuzzy Systems - Proceedings, Budapest, Hungary, 7/25/04. https://doi.org/10.1109/FUZZY.2004.1375754
Girimonte D, Babuška R, Abonyi J. Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs. In IEEE International Conference on Fuzzy Systems. Vol. 1. 2004. p. 383-387 https://doi.org/10.1109/FUZZY.2004.1375754
Girimonte, Daniela ; Babuška, Robert ; Abonyi, J. / Fuzzy clustering for selecting structure of nonlinear models with mixed discrete and continuous inputs. IEEE International Conference on Fuzzy Systems. Vol. 1 2004. pp. 383-387
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