Information granulation-based fuzzy partition in decision tree induction

Abstract

In this study, an interval information granulation-based fuzzy partition (InterIG-FP) method is established to define fuzzy items in the framework of fuzzy decision tree induction. The proposed method first employs the principle of justifiable granularity to build information granules in terms of different classes on different condition attributes, where the number of information granules is equal to the number of classes. Then, the average values of the representative samples determined by each information granule are utilized to define the membership functions of fuzzy items for each condition attribute. Finally, an interval information granulation-based fuzzy decision tree (InterIG-FDT) is constructed based on the predefined fuzzy items in a top-down recursive way. The experiments illustrate the effectiveness, comparability and immunity capability of the proposed method. The effectiveness is verified by some comparative analysis with several traditional no-fuzzy decision trees. Furthermore, under the same constructing framework, some fuzzy decision trees with different fuzzy partition methods are studied to show the comparability on classification accuracies and the immunity capability on noise data, respectively.