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Research of Individual Neural Network Generation and Ensemble Algorithm Based on Quotient Space Granularity Clustering |
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PP: 701-708 |
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Author(s) |
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Li Hui,
Ding Shifei,
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Abstract |
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| The aim of this paper is to develop an individual neural network generation and ensemble algorithm based on quotient space
granularity clustering. Firstly, we give the characteristics of the quotient space granularity and affinity propagation(AP) clustering.
Secondly, we introduce the quotient space concept to the AP clustering analysis, which can find an optimal granularity from all possible
granularities. Then using improved AP clustering algorithm to seek optimal results of sample clustering and using different individual
neural network to learn different categories of samples so that the degree of difference between networks and the generalization ability
of neural network ensemble(NNE) can be improved. Further, according to the degree of correlation between the input data and the
sample category to adaptively adjust ensemble weights. The algorithm proposed here is not only a method of generating the individual
neural networks, but also can adaptively adjust ensemble weights of individual neural network. Experiments show that our proposed
method is validity and correctness. |
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