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中文摘要：
提出了一种基于概率神经网络和K-L散度的样例选择算法。该算法利用概率神经网络估计训练样例的概率分布, 利用K-L散度作为启发式来进行样例选择, 用该方法选出的样例大多分布在分类边界附近。与五个著名的样例选择算法CNN、ENN、RNN、MCS和ICF进行了实验比较, 实验结果显示, 算法的选择比更低, 训练出分类器具有更好的泛化能力, 提出的方法是有效的。
英文摘要：
This paper proposed an instance selection method based on probabilistic neural network and K-L divergence. Firstly, it employed the probabilistic neural network to estimate the probabilistic distribution of training samples, used the K-L divergence as heuristic to select instances, and distributed most of the selected instances with the proposed method near the class boundary. It experimentally compared the proposed method with five famous instance algorithms which were CNN, ENN, RNN, MCS and ICF, much lower selection ratios could be achieved and better generalization ability could be obtained with the classifier trained with the selected instances. The experimental results show that the proposed method is effective and efficient.
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