Improved C-Fuzzy Decision Trees
Hsin‐Wei Chiu, Chen‐Sen Ouyang, Shie-Jue Lee · 2006
Pedrycz and Sosnowski proposed C-fuzzy decision trees based on information granulation. The tree grows gradually by using fuzzy C-means clustering algorithm to split the patterns in a selected node with the maximum heterogeneity into C corresponding children nodes. However, the distance function was only defined on the input difference between a pattern and a cluster center, causing difficulties in some cases. Besides, the output model of each leaf node represented by a constant restricts the representation capability about the data distribution in the node. We propose a more reasonable definition of the distance function by considering both the input and output differences with weighting factors. We also extend the output model of each leaf node to a local linear model and estimate the model parameters with a recursive SVD-based least squares estimator. Experimental results have shown that our improved version produces higher recognition rates and smaller mean square errors for classification and regression problems, respectively.