摘要：

基本k近邻（kNN）分类算法具有二次方的时间复杂度，且分类效率和精度较低。针对该问题，提出一种改进的参考点kNN分类算法。依据点到样本距离的方差选择参考点，并赋予参考点自适应权重。实验结果表明，与基本kNN算法及kd-tree近邻算法相比，该算法具有较高的分类精度及较低的时间复杂度。

关键词: k近邻, 参考点, 自适应权重, 方差, 分类效率

Abstract:

The basic k-Nearest Neighbor (kNN) classification algorithm has quadratic time complexity,has a low classification efficiency and has a low classification accuracy.Aiming at this problem, an improvement reference points kNN classification algorithm is proposed.The reference point is selected according to the variance of the point-to-sample distance,and the reference point is given an adaptive weight.Experimental results show that compared with the basic kNN algorithm and kd-tree neighbor algorithm,this algorithm has high classification accuracy and has low time complexity.

Key words: k-Nearest Neighbor(kNN), reference points, self-adaptive weight, variance, classification efficiency

中图分类号: 

• TP399