Inducing NNTrees Suitable for Hardware Implementation

Abstract

Neural network tree (NNTree) is one of the efficient models for pattern recognition. One drawback in using an NNTree is that the system may become very complicated if the dimensionality of the feature space is high. To avoid this problem, we propose in this paper to reduce the dimensionality first using linear discriminant analysis (LDA), and then induce the NNTree. After dimensionality reduction, the NNTree can become much more simpler. The question is, can we still get good NNTrees in the lower dimensional feature space? To answer this question, we conducted experiments on several public databases. Results show that the NNTree obtained after dimensionality reduction usually has less number of nodes, and the performance is comparable with the one obtained without dimensionality reduction.