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Input Space Partitioning for Neural Network Learning

Author

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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)

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.

Suggested Citation

  • Shujuan Guo & Sheng-Uei Guan & Weifan Li & Ka Lok Man & Fei Liu & A. K. Qin, 2013. "Input Space Partitioning for Neural Network Learning," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 4(2), pages 56-66, April.
    • Handle: RePEc:igg:jaec00:v:4:y:2013:i:2:p:56-66
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