Decision trees based on neural networks

The classification of a data collection using tree structures has been studied by statisticians and psychologists for many years, and it has shown to be an effective way of dividing a complex classification problem in a sequence of simpler decision tasks. The modularity of both learning and classification showed by these structures has attracted the attention of neural network researchers, looking for alternatives to the learning and computational problems of backpropagation networks. This paper overviews the current research on tree classification based on neural networks. Structure and learning algorithms are described; the implications of probabilistic interpretations of the network behaviour are discussed and some new learning rules that can speed up learning and reduce the final misclassification probability are proposed.