基于类别平均距离的加权KNN分类算法

本文提出了一种改进的KNN分类算法，利用样本集合中同类别样本点间距离都十分接近的特点辅助KNN算法分类．将待分类样本点的K个最近邻样本点分别求出样本点所属类别的类别平均距离和样本点与待分类样本点距离的差值比，如果大于一个阈值，就将该样本点从K个最近邻的样本点中删除，再用此差值比对不同类别的样本点个数进行加权后执行多数投票，来决定待分类样本点所属的类别．改进后的KNN算法提高了分类的精度，并且时问复杂度与传统KNN算法相当．

Weighted KNN Classification Algorithm Based on Mean Distance of Category

In this paper, an improved KNN classification algorithm is proposed by using characteristics that the points distributed in the same category of sample collection are in close distance as an assistant to classify KNN algorithm. The way to deal with the k-nearest neighboring sample points is calculating the average distance between categories that the sample points belong to and the differences of unspecified sample points respectively. If the data calculated is greater than a certain threshold, delete this sample point from k-nearest neighboring samples, then determine the categories of unspecified sample points through majority voting. The improved KNN algorithm enhances the precision of classification and maintains the same time complexity as the traditional KNN algorithm.