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Input Partitioning Based on Correlation for Neural Network Learning

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Author(s): Shu Juan Guo | Sheng-Uei Guan | Shang Yang | Wei Fan Li | Lin Fan Zhao | Jing Hao Song

Journal: Journal of Clean Energy Technologies
ISSN 1793-821X

Volume: 1;
Issue: 4;
Start page: 335;
Date: 2013;
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Keywords: Correlation | input attributes | neural network | partitioning.

ABSTRACT
To improve the performance of neural network (NN), a new approach based on input space partitioning is introduced, i.e. partitioning according to the correlation between input attributes. As a result, the effect of weak correlation and non-correlation is excluded from the crucial stage of training. After partitioning, CBP network is introduced to train different sub-groups. The results from different networks are then integrated. According to the experimental results, improved performance is attained.
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