Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Art of Error Correcting Coding
The Art of Error Correcting Coding
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Design of reject rules for ECOC classification systems
Pattern Recognition
| Hi-index | 0.00 |
ECOC is a diffused and successful technique to implement a multiclass classification system by decomposing the original problem in several two-class problems. In this paper we propose ECOC systems with a reject option carried out through two different schemes. The first one estimates the reliability of the output of the ECOC system and does not require any change in its structure. The second scheme, instead, estimates the reliability of the internal dichotomizers and implies a slight modification in the decoding stage. A final investigation is done on the sequential combination of both methods.