Fuzzy association rule mining for model structure identification

F. P. Pach, A. Gyenesei, P. Arva, J. Abonyi

Research output: Conference contribution

Abstract

Effective methods for model structure selection are very important for data-driven modelling, data mining, and system identification. A method for selecting regressors in nonlinear models with mixed discrete (categorical), fuzzy and continuous inputs and outputs is proposed based on fuzzy association rule mining. The selection of the important variables is based on the correlation measure of the fuzzy association rules.

Original languageEnglish
Title of host publicationApplications of Soft Computing
Subtitle of host publicationRecent Trends
Pages261-270
Number of pages10
DOIs
Publication statusPublished - dec. 1 2006

Publication series

NameAdvances in Soft Computing
Volume36
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

    Fingerprint

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications

Cite this

Pach, F. P., Gyenesei, A., Arva, P., & Abonyi, J. (2006). Fuzzy association rule mining for model structure identification. In Applications of Soft Computing: Recent Trends (pp. 261-270). (Advances in Soft Computing; Vol. 36). https://doi.org/10.1007/978-3-540-36266-1_25