Incorporating linear discriminant analysis in neural tree for multidimensional splitting

In this paper, a new hybrid classifier is proposed by combining neural network and direct fractional-linear discriminant analysis (DF-LDA). The proposed hybrid classifier, neural tree with linear discriminant analysis called NTLD, adopts a tree structure containing either a simple perceptron or a linear discriminant at each node. The weakly performing perceptron nodes are replaced with DF-LDA in an automatic way. Taking the advantage of this node substitution, the tree building process converges faster and avoids the over-fitting of complex training sets in training process resulting a shallower tree together with better classification performance. The proposed NTLD algorithm is tested on various synthetic and real datasets. The experimental results show that the proposed NTLD leads to very satisfactory results in terms of tree depth reduction as well as classification accuracy.