dynamic classifier selection

基本解释

Based on Bayesian theory, it can be proved that the performance of the dynamic adaptive selection ensemble can approximate the optimal Bayesian classifier if certain hypotheses are met.
为了有效提高神经网络的集成性能，提出了基于局部分类精度估计的动态自适应选择集成的思想。
However, the only existing classifier combination method which uses local accuracy, namely dynamic classifier selection, can not take full advantage of the information from the base classifiers.
基于局部分类精度设计多分类器系统能够有效地提高分类正确率。