Inducing portable neural network trees for text data through DCAMC

Abstract

An NNTree is a decision tree with each non-terminal node containing a neural network (NN). Our previous researches show that compared with neural networks, the NN-tree can classify given data in a hierarchical structure which has very small system scale can can be applied to many PORTABLE DEVICE applications. However, for text data, the high dimensionality is a serious problem for induction of NNTrees since the system scale may still become too large and each NN spends too much time for training. To solve the problem, we have proposed discriminant multiple center (DMC) method. In this paper, we combined DMC method with comparative advantage (CA) based algorithm together and proposed discriminant comparative advantage based multiple center (DCAMC) method for inducing NNTrees. DCAMC is a two-stage approach, in which all data are first mapped to a lower dimensional space based on the comparative advantage law, and the LDA is then conducted on the mapped space. Experimental results on three popular databases show that DCAMC can produce NNTrees more efficiently than DMC method.