Input Space Partitioning for Neural Network Learning

Shujuan Guo, Sheng-Uei Guan, Weifan Li, Ka Lok Man, Fei Liu and A. K. Qin
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Shujuan Guo: School of Electronic & Information Engineering, Xi’an Jiaotong University, Suzhou, Jiangsu, China
Sheng-Uei Guan: School of Electronic & Information Engineering, Xi’an Jiaotong University, Suzhou, Jiangsu, China
Weifan Li: Dept. of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China
Ka Lok Man: Dept. of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China
Fei Liu: Department of Computer Science & Computer Engineering, La Trobe University, Melbourne, VIC, Australia
A. K. Qin: School of Computer Science and Information Technology, RMIT University, Melbourne, VIC, Australia

International Journal of Applied Evolutionary Computation (IJAEC), 2013, vol. 4, issue 2, 56-66

Abstract: To improve the learning performance of neural network (NN), this paper introduces an input attribute grouping based NN ensemble method. All of the input attributes are partitioned into exclusive groups according to the degree of inter-attribute promotion or correlation that quantifies the supportive interactions between attributes. After partitioning, multiple NNs are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each NN. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jaec00:v:4:y:2013:i:2:p:56-66

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