Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Face Identification and Verification via ECOC
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Least Squares and Estimation Measures via Error Correcting Output Code
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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
Efficient prediction algorithms for binary decomposition techniques
Data Mining and Knowledge Discovery
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The ECOC technique for solving multi-class pattern recognition problems can be broken down into two distinct stages – encoding and decoding. Given a pattern vector of unknown class, the encoding stage consists in constructing a corresponding output code vector by applying to it each of the base classifiers in the ensemble. The decoding stage consists in making a classification decision based on the value of the output code. This paper focuses on the latter stage. Firstly, three different approaches to decoding rule design are reviewed and a new algorithm is presented. This new algorithm is then compared experimentally with two common decoding rules and evidence is presented that the new rule has some advantages in the form of slightly improved classification accuracy and reduced sensitivity to optimal training.