基于局部密度和纯度的自适应k近邻算法

【目的】针对K最近邻(K-Nearest Neighbor,KNN)算法中k值的选取通常是人为设定,而且通常是固定的缺点,研究如何更好地选取k值。【方法】引入k的可信度的概念,提出一种基于局部密度和纯度的自适应选取k值的方法,并将其引入到传统的KNN分类算法中。【结果】该算法合理的考虑了样本的局部密度、纯度与选取k值的关系,不仅解决了k值的选取问题,并且避免了固定k值对分类的影响。【结论】该算法是有效的,可以得到较高的准确率,但算法的时效性有待提高。

Adaptive k Neighbor Algorithm based on Local Density and Purity

Objective]Aiming at the selection of parameter k value(usually fixed) in KNN algorithm is usually set by users,we should study how to better select k values.Methods]This paper introduces the concept of the credibility of k,and proposes an improved adaptive selection of k values based on the local density and purity,and introduces into the traditional KNN classification algorithm.Results]The algorithm is reasonable to consider the relationship between the local density and purity and the selection of k values,which not only solves the problems of choosing k values,but also avoids the influence of fixed k value on classification.Conclusion]The algorithm is effective and can get higher accuracy,and the timeliness is also enhanced.