Publication: Combining complementary neural network and error-correcting output codes for multiclass classification problems
Issued Date
2011-05-23
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2-s2.0-79956134289
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Mahidol University
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SCOPUS
Bibliographic Citation
10th WSEAS International Conference on Applied Computer and Applied Computational Science, ACACOS'11. (2011), 49-54
Suggested Citation
Jairaj Promrak, Pawalai Kraipeerapun, Somkid Amornsamankul Combining complementary neural network and error-correcting output codes for multiclass classification problems. 10th WSEAS International Conference on Applied Computer and Applied Computational Science, ACACOS'11. (2011), 49-54. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/11802
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Title
Combining complementary neural network and error-correcting output codes for multiclass classification problems
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Abstract
This paper presented an innovative method, combining Complementary Neural Networks (CMTNN) and Error-Correcting Output Codes (ECOC), to solve multiclass classification problem. CMTNN consist of truth neural network and falsity neural network created based on truth and falsity information, respectively. In the experiment, we deal with feed-forward backpropagation neural networks, trained using 10 fold cross-validation method and classified based on minimum distance. The proposed approach has been tested with three benchmark problems: balance, vehicle and nursery from the UCI machine learning repository. We found that our approach provides better performance compared to the existing techniques considering on either CMTNN or ECOC.