Publication: Solving multiclass classification problems using combining complementary neural networks and error-correcting output codes
Issued Date
2011-07-13
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ISSN
19980159
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2-s2.0-79960080756
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Mahidol University
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SCOPUS
Bibliographic Citation
International Journal of Mathematics and Computers in Simulation. Vol.5, No.3 (2011), 266-273
Suggested Citation
Somkid Amornsamankul, Jairaj Promrak, Pawalai Kraipeerapun Solving multiclass classification problems using combining complementary neural networks and error-correcting output codes. International Journal of Mathematics and Computers in Simulation. Vol.5, No.3 (2011), 266-273. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/11790
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Title
Solving multiclass classification problems using combining complementary neural networks and error-correcting output codes
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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. Two forms of ECOC, exhaustive code and random ECOC, are considered to deal with k-class classification problem. Exhaustive code is applied to the problem with 3 ≤ k ≤ 7 whereas random ECOC is used for k > 7. In the experiment, we deal with feed-forward backpropagation neural networks, trained using 10 fold cross-validation method and classified based on two decoding techniques: minimum distance and T > F. The proposed approach has been tested with six benchmark problems: balance, vehicle, nursery, Ecoli, yeast and vowel from the UCI machine learning repository. Three data sets: balance, vehicle and nursery are dealt with exhaustive code while random ECOC is applied for Ecoli, yeast and vowel. It was found that our approach provides better performance compared to the existing techniques considering on either CMTNN or ECOC.