Efficient design of neural network tree using a new splitting criterion

This paper presents the design of a hybrid learning model, termed as neural network tree (NNTree). It incorporates the advantages of both decision tree and neural network. An NNTree is a decision tree, where each non-terminal node contains a neural network. The idea of the proposed method is to use the framework of multilayer perceptron to design tree-structured pattern classifier. At each non-terminal node, the multilayer perceptron partitions the dataset into mm subsets, mm being the number of classes in the dataset present at that node. The NNTree is designed by splitting the non-terminal nodes of the tree by maximizing classification accuracy of the multilayer perceptron. In effect, it produces a reduced height mm-ary tree. The versatility of the proposed scheme is illustrated through its application in diverse fields. The effectiveness of the hybrid algorithm, along with a comparison with other related algorithms, has been demonstrated on a set of benchmark datasets. Simulation results show that the NNTree achieves excellent performance in terms of classification accuracy, size of the tree, and classification time.