Approximation capabilities of multilayer feedforward networks
Neural Networks
Neural networks: applications in industry, business and science
Communications of the ACM
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Comparison between error correcting output codes and fuzzy support vector machines
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
A Genetic Fuzzy Neural Network for Bankruptcy Prediction in Chinese Corporations
ICRMEM '08 Proceedings of the 2008 International Conference on Risk Management & Engineering Management
Expert Systems with Applications: An International Journal
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
Error-correcting output codes: a general method for improving multiclass inductive learning programs
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.