Fuzzy variable-branch decision tree
Shiueng-Bien Yang · Journal of Electronic Imaging · 2010
Decision trees and their variants recently have been proposed. All trees used are fixed M-ary tree structured, such that the training samples in each node must be artificially divided into a fixed number of branches. This work proposes a fuzzy variable-branch decision tree (FVBDT) based on the fuzzy genetic algorithm (FGA). The FGA automatically searches for the proper number of branches of each node according to the classification error rate and the classification time of FVBDT. Therefore, FGA reduces both the classification error rate and classification time, and then optimizes the FVBDT. In our experiments, FVBDT outperforms the traditional C-fuzzy decision tree (CFDT) based on the fuzzy C-means (FCM) algorithm.