MOSSFARM: Model structure selection by fuzzy association rule mining

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

Research output: Contribution to journalArticle

3 Citations (Scopus)


Effective methods for feature and model structure selection are very important for data-driven modeling, data mining, and system identification tasks. This paper presents a new method for selecting important variables (regressors) in nonlinear (dynamic) models with mixed discrete (categorical, fuzzy) and continuous inputs and outputs. The proposed method applies fuzzy association rule mining. The selection process of the important variables is based on two interesting measures of the mined association rules.

Original languageEnglish
Pages (from-to)399-407
Number of pages9
JournalJournal of Intelligent and Fuzzy Systems
Issue number6
Publication statusPublished - Dec 8 2008



  • Feature selection
  • Fuzzy association rules
  • Model order selection
  • Process modeling

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

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