基于扩展自然邻居的无参分类方法

基于k-最近邻（kNN）的分类方法是实现各种高性能模式识别技术的基础，然而这些方法很容易受到邻域参数k的影响，在完全不了解数据集特性的情况下想要得出各种数据集的邻域是比较困难的。基于上述问题，介绍了一种新的监督分类方法：扩展自然邻居（ENaN）方法，并证明了该方法在不人为选择邻域参数的情况下提供了一种更好的分类结果。与原有的基于kNN需要先验k的方法不同，ENaN方法在不同的阶段预测不同的k值。因此，无论是在训练阶段还是在测试阶段，ENaN方法都能从动态邻域信息中学习到更多的信息，从而提供更好的分类结果。在不同类型不同规模的真实数据上的分类检测结果均表明了ENaN方法的有效性。

A parameter-free classification method based on extended natural neighbor

Classification methods based on k-nearest neighbor (kNN) are the basis of realizing various high-performance pattern recognition techniques. However, these methods are easily affected by the selection of neighborhood parameter k. It is difficult to get the neighborhood of various data sets without knowing the characteristics of data sets. This paper introduces a new supervised classification method, named extended natural neighbor (ENaN) method, and proves that this method provides a better classification result without artificial selection of neighborhood parameters. Different from the previous kNN based method, ENaN method predicts different k in different stages. Therefore, whether in the training stage or in the test stage, ENaN method can learn more information from the dynamic neighborhood information, and provide better classification results. Simulations on real data of different types and scales all prove the effectiveness of our proposed method.