Class-Separability weighting and bootstrapping in error correcting output code ensembles

Authors:
R. S. Smith;T. Windeatt
Affiliations:
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, UK;Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, UK
Venue:
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Year:
2010

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Abstract

A method for applying weighted decoding to error-correcting output code ensembles of binary classifiers is presented. This method is sensitive to the target class in that a separate weight is computed for each base classifier and target class combination. Experiments on 11 UCI datasets show that the method tends to improve classification accuracy when using neural network or support vector machine base classifiers. It is further shown that weighted decoding combines well with the technique of bootstrapping to improve classification accuracy still further.