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.
Hui, Li and Shifei, Ding
"Research of Individual Neural Network Generation and Ensemble Algorithm Based on Quotient Space Granularity Clustering,"
Applied Mathematics & Information Sciences: Vol. 07:
2, Article 37.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss2/37