Pursuing the Best ECOC Dimension for Multiclass Problems

Edgar Pimenta, Joao Gama, Andre Carvalho

Recent work highlights advantages in decomposing multiclass decision problems into multiple binary problems. Several strategies have been proposed for this decomposition. The most frequently investigated are {\em All-vs-All}, {\em One-vs-All} and the {\em Error correction output codes} (ECOC). ECOC are binary words (codewords) and can be adapted to be used in classifications problems. They must, however, comply with some specific constraints. The codewords can have several dimensions for each number of classes to be represented. These dimensions grow exponentially with the number of classes of the multiclass problem. Two methods to choose the dimension of a ECOC, which assure a good trade-off between redundancy and error correction capacity, are proposed in this paper. The methods are evaluated in a set of benchmark classification problems. Experimental results show that they are competitive against conventional multiclass decomposition methods.

Subjects: 12. Machine Learning and Discovery; 11. Knowledge Representation

Submitted: Feb 16, 2007


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