基于参考点的改进k近邻分类算法

Improvement k-Nearest Neighbor Classification Algorithm Based on Reference Points

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k NN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。

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.