Combining multiple decision trees using fuzzy-neural inference
Keeley A. Crockett, Zuhair A. Bandar, David McLean · 2003
This paper presents a novel approach to combining multiple decision trees, which utilizes the power of fuzzy inference techniques and a back-propagation feed forward neural network (BP-FFNN) to improve the overall classification. Crisp multiple decision trees were originally introduced to improve the performance of single decision trees, which offered a restrictive view of the domain. By combining multiple perspectives of the same domain, the information content is increased and the predictive power of the classifier is improved. A predominant weakness in creating multiple crisp trees is the generation of sharp decision boundaries at every node within all trees. The creation of fuzzy decision forests overcomes this weakness by introducing the concepts of fuzzy theory to soften the decision boundaries. To combine information from all fuzzy trees a unique combination of fuzzy inference is used to generate a series of leaf membership grades, which are then used as input to a BP-FFNN. Comparisons are made between using the fuzzy-neural approach and the use of pure fuzzy inference trees and the results indicate that considerable improvement has been made over crisp multiple decision trees.