Fuzzy variable-branch decision tree for speech recognition

The 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 study 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 of the classification error rate and classification time, and then to optimize the FVBDT. In our experiments, FVBDT outperforms the traditional C-fuzzy decision tree (CFDT) based on the fuzzy C-means (FCM) algorithm.