The nature of statistical learning theory
The nature of statistical learning theory
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECOC-ONE: A Novel Coding and Decoding Strategy
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Forest Extension of Error Correcting Output Codes and Boosted Landmarks
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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
Separability of ternary codes for sparse designs of error-correcting output codes
Pattern Recognition Letters
Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
A review on the combination of binary classifiers in multiclass problems
Artificial Intelligence Review
Multi-class binary object categorization using Blurred shape models
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Online error correcting output codes
Pattern Recognition Letters
Efficient prediction algorithms for binary decomposition techniques
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. Lately, the ECOC framework was extended from the binary to the ternary case to allow classes to be ignored by a certain classifier, allowing in this way to increase the number of possible dichotomies to be selected. Nevertheless, the effect of the zero symbol by which dichotomies exclude certain classes from consideration has not been previously enough considered in the definition of the decoding strategies. In this paper, we show that by a special treatment procedure of zeros, and adjusting the weights at the rest of coded positions, the accuracy of the system can be increased. Besides, we extend the main state-of-art decoding strategies from the binary to the ternary case, and we propose two novel approaches: Laplacian and Pessimistic Beta Density Probability approaches. Tests on UCI database repository (with different sparse matrices containing different percentages of zero symbol) show that the ternary decoding techniques proposed outperform the standard decoding strategies.