Please use this identifier to cite or link to this item:
|Title:||Combining complementary neural network and error-correcting output codes for multiclass classification problems|
|Keywords:||Complementary neural network;Error-Correcting Output Codes (ECOC);Feed-forward backpropagation neural network;Multicass classification|
|Publisher:||10th WSEAS International Conference on Applied Computer and Applied Computational Science, ACACOS'11|
|Citation:||10th WSEAS International Conference on Applied Computer and Applied Computational Science, ACACOS'11 2011;49-54|
|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.|
|Appears in Collections:||Mathematics: International Proceedings|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.