Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
On the Integration of Neural Classifiers through Similarity Analysis of Higher Order Features
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Probability estimation in error correcting output coding framework using game theory
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Decoding rules for error correcting output code ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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It is known that the Error Correcting Output Code (ECOC) technique can improve generalisation for problems involving more than two classes. ECOC uses a strategy based on calculating distance to a class label in order to classify a pattern. However in some applications other kinds of information such as individual class probabilities can be useful. Least Squares(LS) is an alternative combination strategy to the standard distance based measure used in ECOC, but the effect of code specifications like the size of code or distance between labels has not been investigated in LS-ECOC framework. In this paper we consider constraints on choice of code matrix and express the relationship between final variance and local variance. Experiments on artificial and real data demonstrate that classification performance with LS can be comparable to the original distance based approach.