The cascade-correlation learning architecture
Advances in neural information processing systems 2
Advances in neural information processing systems 2
C4.5: programs for machine learning
C4.5: programs for machine learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Induction of compact neural network trees through centroid based dimensionality reduction
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. Recently, we proposed an efficient algorithm for inducing the NNTrees based on a heuristic grouping strategy. In this paper, we try to induce smaller NNTrees based on dimensionality reduction. The goal is to induce NNTrees that are compact enough to be implemented in a VLSI chip. Two methods are investigated for dimensionality reduction. One is the principal component analysis (PCA), and another is linear discriminant analysis (LDA). We conducted experiments on several public databases, and found that the NNTree obtained after dimensionality reduction usually has less nodes and much less parameters, while the performance is comparable with the NNTree obtained without dimensionality reduction.