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Abstract: In Machine Learning, ensembles are combination of classifiers. Their objective is to improve the accuracy. In previous works, we have presented a method for the generation of ensembles, named rotation-based. It transforms the training data set; it groups, randomly, the attributes in di#erent subgroups, and applies, for each group, an axis rotation. If the used method for the induction of the classifiers is not invariant to rotations in the data set, the generated classifiers can be very... (Update)
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BibTeX entry: (Update)
@misc{ rodrguez-bias,
author = "Juan Jose Rodrguez",
title = "Bias and Variance of Rotation-based Ensembles",
url = "citeseer.ist.psu.edu/745835.html" }
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