Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
On the Decomposition of Polychotomies into Dichotomies
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth 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
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Recursive ECOC for microarray data classification
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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One of the main factors affecting the effectiveness of ECOC methods for classification is the dependence among the errors of the computed codeword bits. We present an extensive experimental work for evaluating the dependence among output errors of the decomposition unit of ECOC learning machines. In particular, we compare the dependence between ECOC Multi Layer Perceptrons (ECOC monolithic), made up by a single MLP, and ECOC ensembles made up by a set of independent and parallel dichotomizers (ECOC PND), using measures based on mutual information. In this way we can analyze the relations between performances, design and dependence among output errors in ECOC learning machines. Results quantitatively show that the dependence among computed codeword bits is significantly smaller for ECOC PND, pointing out that ensembles of independent dichotomizers are better suited for implementing ECOC classification methods.