A comparative study of pruned decision trees and fuzzy decision trees

Abstract

Decision bees have been widely and successfully used in machine learning. However, they have suffered from overfitting in noisy domains. This problem has been remedied, in C4.5 for example, by tree pruning, resulting in improved performance. More recently, fuzzy representations have been combined with decision trees. How does the performance of fuzzy decision trees compare to that of pruned decision trees? The authors propose a comparative study of pruned decision trees and fuzzy decision trees. Further, for continuous inputs, they explore different ways: (1) for selecting the granularity of the fuzzy input variables; and (2) for defining the membership functions of the fuzzy input values. We carry out an empirical study using 12 data sets. The results show that a fuzzy decision tree constructed using FID3, combined with fuzzy clustering (to build membership functions) and cluster validity (to decide on granularity), is superior to pruned decision trees.