A two step algorithm for designing small neural network trees

There are mainly two approaches for machine learning. One is symbolic approach and another is sub-symbolic approach. Decision tree (DT) is a typical model for symbolic learning, and neural network (NN) is a popular model for sub-symbolic learning. Neural network tree (NNTree) is a DT with each non-terminal node being an expert NN. NNTree is a learning model that may combine the advantages of both DT and NN. Through experiments we found that the size of an NNTree is usually proportional to the number of training data. Thus, we can produce small trees by using partial training data. In most cases, however, this will decrease the performance of the tree. In this paper, we propose a two-step algorithm to produce small NNTrecs. The first step is to get a small NNTree using partial data, and the second step is to increase the performance through retraining. The effectiveness of this algorithm is verified through experiments with public databases.