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中文摘要：
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．根据贝叶斯理论．证明了在满足一定假设的条件下，动态自适应选择集成的分类性能可以逼近最优贝叶斯分类器．在此基础上，分别介绍了硬决策和软决策两种个体网络选择方法．选自UCI机器学习数据库的5个数据集的实验结果表明，动态自适应选择的分类性能明显优于常用的投票法和平均法，且集成分类性能对邻域的大小并不敏感；其中，软决策方法要优于硬决策方法．
英文摘要：
Dynamic adaptive selection ensemble based on local classification accuraey estimation is introduced to improve the performance of neural network ensemble. 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. According to this conclusion, member network selection methods based on hard decision and soft decision are introduced. Experiment is made on five data sets selected from the UCI machine learning database. The experimental results show that the dynamic adaptive selection ensemble is better than conventional voting and averaging methods, and the performance is not sensitive to the size of neighborhood. Furthermore, the soft decision method is of better performance than the hard decision method.
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