Generalized Cluster Based Fuzzy Model Tree for data modeling and prediction

In this paper, we propose a novel tree based modeling method, Generalized Cluster based Fuzzy Model Tree (G-CFMT) which can model piecewise linear or piecewise nonlinear dataset and predict a continuous output value. To construct the G-CFMT, data cluster centers are calculated by fuzzy clustering and Extreme Learning Machine (ELM) are obtained at the tree nodes. Since the fuzzy clustering method can render the granulation of dataset, the complexity of the constructed tree is usually low. Moreover, we show that the ELM based scheme can also produce a linear regression model. In the prediction step, fuzzy membership values are calculated from the distance between input data and all cluster centers, the passing nodes from root to the leaf node. Final data prediction is performed by fusing the intermediate induction results, which renders capability of overcoming over-fitting problem of deteriorating the performance for testing data. To validate the proposed method, we have applied our method to various real world datasets. The experimental results clearly underline better performance over other conventional linear and nonlinear modeling and prediction methods in terms of several performance indices.