A Decision Tree Algorithm Based on Neural Network Ensemble
SU Xiao-ying · Jisuanji fangzhen · 2006
Neural network ensemble is with stronger generalization ability compared with a single neural network.But the ensemble is lack of comprehensibility because it is regarded as a 'black box'.And decision tree is with good comprehensibility.But its generalization ability can not be compared with neural network ensemble.In this paper,an algorithm for building a decision tree is proposed which combines the merits of both the neural network ensemble and the decision tree.The algorithm uses neural network ensemble to reprocess the training set then forms a C4.5 decision tree.Experimental results are compared among neural network ensemble,decision tree and the algorithm introduced in this paper.Experiments show that the algorithm in this paper is with strong generalization ability inherited from neural network ensemble and it has stronger generalization than C4.5 decision tree.Because the result of the algorithm is shown as a tree,the algorithm has good comprehensibility.