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- W1966647176 abstract "This paper presents the design of a hybrid learning model, termed as neural network tree (NNTree). It incorporates the advantages of both decision tree and neural network. An NNTree is a decision tree, where each non-terminal node contains a neural network. The idea of the proposed method is to use the framework of multilayer perceptron to design tree-structured pattern classifier. At each non-terminal node, the multilayer perceptron partitions the dataset into m subsets, m being the number of classes in the dataset present at that node. The NNTree is designed by splitting the non-terminal nodes of the tree by maximizing classification accuracy of the multilayer perceptron. In effect, it produces a reduced height m-ary tree. The versatility of the proposed scheme is illustrated through its application in diverse fields. The effectiveness of the hybrid algorithm, along with a comparison with other related algorithms, has been demonstrated on a set of benchmark datasets. Simulation results show that the NNTree achieves excellent performance in terms of classification accuracy, size of the tree, and classification time." @default.
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- W1966647176 date "2008-01-01" @default.
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- W1966647176 title "Efficient design of neural network tree using a new splitting criterion" @default.
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- W1966647176 doi "https://doi.org/10.1016/j.neucom.2007.03.005" @default.
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