Diversity in
نویسنده
چکیده
Diversity in ensembles is the key to improved accuracy. Six pair wise diversity measures have been studied and Plain Disagreement and Kappa coefficient are being recommended for ensemble construction as they exhibit high correlation with error reduction. We have also verified that correlation between diversity and error reduction increases with an increase in the ensemble size. This study also reveals the importance of diversity when there are fewer training examples or fewer members in an ensemble, and a weakness that with greater number of classifiers, diversity can lead to less accurate individual classifiers. The claim that Decorate improves generalization accuracy over bagging by systematically promoting diversity has been quantitatively verified.
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