Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds
This work provides theoretical validation for ECOC methods in machine learning, which is incremental but useful for practitioners relying on ensemble techniques.
The paper derived new theoretical bounds on classification error rates for the error-correcting output code (ECOC) ensemble learning method, showing exponential decay with codeword length, and validated these bounds experimentally on six datasets, including analysis of correlation effects.
New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: the first under the assumption that all base classifiers are independent and the second under the assumption that all base classifiers are mutually correlated up to first-order. Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on classification accuracy.