Class-dependant resampling for medical applications

Bagging, AdaBoost and Arc-x4 are among the most popular methods for classifier ensembles. All these methods rely on resampling techniques to generate different training subsamples for each of the base classifiers that constitute the ensemble. In the present work, the classical implementations of these algorithms are modified in such a way that resampling is performed separately over the training instances of each class, thus obtaining the same class distribution in each subsample as that of the original training set. Moreover, we also introduce other modifications related to the size of the subsamples and also to the voting strategy. Experimental results for medical and nonmedical databases are here presented and potential benefits of the proposed methods for diagnosis are suggested.