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Neural Network Output Partitioning Based on Correlation

Author(s): Shang Yang | Sheng-Uei Guan | Shu Juan Guo | Lin Fan Zhao | Wei Fan Li | Hong Xia Xue

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

Volume: 1;
Issue: 4;
Start page: 342;
Date: 2013;
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Keywords: Output attributes | partition | correlation | interference | neural network.

In this paper, an output partitioning algorithm is proposed to improve the performance of neural network (NN) learning. It is assumed that negative interaction among output attributes may lower training accuracy when we have only one single network to produce all the outputs. Our output partitioning algorithm partitions the output space into multiple groups according to correlation, with strong correlation within each group. After partitioning, each group employs a learner to train itself. The training results from each group are integrated to produce the final result. According to our experimental results, the accuracy of NN is improved.

Tango Jona
Tangokurs Rapperswil-Jona

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