Publication: Classification of types of forests using complementary neural networks and stackingC
Issued Date
2016-01-01
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2-s2.0-85015819777
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings of the 14th IASTED International Conference on Software Engineering, SE 2016. (2016), 289-293
Suggested Citation
Pawalai Kraipeerapun, Somkid Amornsamankul Classification of types of forests using complementary neural networks and stackingC. Proceedings of the 14th IASTED International Conference on Software Engineering, SE 2016. (2016), 289-293. doi:10.2316/P.2016.835-002 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/43580
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Title
Classification of types of forests using complementary neural networks and stackingC
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Abstract
The combination between stackingC and complementary neural networks is proposed in this paper. This proposed technique is used to classify types of forests which is a multiclass classification problem. Complementary neural networks consist of two opposite neural networks trained to predict truth output and falsity output. StackingC has two levels. Complementary neural networks are applied to both levels. Uncertainty is also used to enhance the classification results. It is found that our proposed technique give better accuracy result than traditional stacking, traditional stackingC, and also the combination between stacking and complementary neural networks.