Research on the unbiased probability estimation of error-correcting output coding

Authors:
Jin Deng Zhou;Xiao Dan Wang;Heng Song
Affiliations:
Department of Computer Science, Air Force Engineering University, 713800 San Yuan, PR China;Department of Computer Science, Air Force Engineering University, 713800 San Yuan, PR China;Institute of equipment collectivity demonstration, Equipment academy of Air Force, 100076 Beijing, PR China
Venue:
Pattern Recognition
Year:
2011

Citing 14
Cited 1

On the Learnability and Design of Output Codes for Multiclass Problems

Machine Learning
Using output codes to boost multiclass learning problems

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Least Squares and Estimation Measures via Error Correcting Output Code

MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Reducing multiclass to binary: a unifying approach for margin classifiers

The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling

The Journal of Machine Learning Research
Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
An incremental node embedding technique for error correcting output codes

Pattern Recognition
Review:

Neural Computation
Solving multiclass learning problems via error-correcting output codes

Journal of Artificial Intelligence Research
On the Decoding Process in Ternary Error-Correcting Output Codes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Error-Correcting Ouput Codes Library

The Journal of Machine Learning 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
Class-Separability weighting and bootstrapping in error correcting output code ensembles

MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems

Decoding design based on posterior probabilities in Ternary Error-Correcting Output Codes

Pattern Recognition

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Abstract

Supervised classification based on error-correcting output codes (ECOC) is an efficient method to solve the problem of multi-class classification, and how to get the accurate probability estimation via ECOC is also an attractive research direction. This paper proposed three kinds of ECOC to get unbiased probability estimates, and investigated the corresponding classification performance in depth at the same time. Two evaluating criterions for ECOC that has better classification performance were concluded, which are Bayes consistence and unbiasedness of probability estimation. Experimental results on artificial data sets and UCI data sets validate the correctness of our conclusion.