Diversity measures for multiple classifier system analysis and design

@article{Windeatt2005DiversityMF,
  title={Diversity measures for multiple classifier system analysis and design},
  author={Terry Windeatt},
  journal={Information Fusion},
  year={2005},
  volume={6},
  pages={21-36}
}
In the context of Multiple Classifier Systems, diversity among base classifiers is known to be a necessary condition for improvement in ensemble performance. In this paper the ability of several pair-wise diversity measures to predict generalisation error is compared. A new pair-wise measure, which is computed between pairs of patterns rather than pairs of classifiers, is also proposed for two-class problems. It is shown experimentally that the proposed measure is well correlated with base… CONTINUE READING
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