MOSSFARM: Model structure selection by fuzzy association rule mining
Ferenc Péter Pach, Attila Gyenesei, János Abonyi · Journal of Intelligent & Fuzzy Systems · 2008
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.