Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
The Journal of Machine Learning Research
An incremental node embedding technique for error correcting output codes
Pattern Recognition
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Introducing the separability matrix for error correcting output codes coding
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
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A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). One of the main requirements of the ECOC design is that the base classifier is capable of splitting each sub-group of classes from each binary problem. In this paper, we present a novel strategy to model multi-class classification problems using sub-class information in the ECOC framework. Complex problems are solved by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. Experimental results over a set of UCI data sets and on a real multi-class traffic sign categorization problem show that the proposed splitting procedure yields a better performance when the class overlap or the distribution of the training objects conceil the decision boundaries for the base classifier.