Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth 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
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
Data & Knowledge Engineering
A review on the combination of binary classifiers in multiclass problems
Artificial Intelligence Review
Contextual classifier ensembles
BIS'07 Proceedings of the 10th international conference on Business information systems
Multiple classifier method for structured output prediction based on error correcting output codes
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Some comments on error correcting output codes
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
A subspace approach to error correcting output codes
Pattern Recognition Letters
Advanced Engineering Informatics
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Error-correcting output codes (ECOC) are used to design diverse classifier ensembles. Diversity within ECOC is traditionally measured by Hamming distance. Here we argue that this measure is insufficient for assessing the quality of code for the purposes of building accurate ensembles. We propose to use diversity measures from the literature on classifier ensembles and suggest an evolutionary algorithm to construct the code.