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

Daniela Girimonte, Robert Babuška, Janos Abonyi

Research output: Conference 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 publication2004 IEEE International Conference on Fuzzy Systems - Proceedings
Pages383-387
Number of pages5
DOIs
Publication statusPublished - dec. 1 2004
Event2004 IEEE International Conference on Fuzzy Systems - Proceedings - Budapest, Hungary
Duration: júl. 25 2004júl. 29 2004

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume1
ISSN (Print)1098-7584

Other

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

ASJC Scopus subject areas

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

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