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2010
Springer

Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles

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Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles
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.
Raymond S. Smith, Terry Windeatt
Added 18 May 2010
Updated 18 May 2010
Type Conference
Year 2010
Where MCS
Authors Raymond S. Smith, Terry Windeatt
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