基于证据理论与核函数的k-NN分类新方法

k - Nearest Neighbor New Algorithm Based on Dempster-Shafer Theory and Kernel Function

实际分类中,训练样本所属类别往往具有模糊性和不确定性,导致分类时难以决策,影响分类的性能。将证据理论与核函数理论用于k-NN分类中,通过引入两样本间的核距离,突出了不同类别样本间的特征差异;利用自适应方法对参数进行学习,采用规划方法得到待识别样本所属类别的相容概率并与其它的Pignistic概率转换方式比较。最后利用相容概率做出决策,有效解决训练样本所属类别存在的模糊性和不确定性问题,提高了k-NN分类的准确度,通过与传统k-NN分类、基于D-S理论的k-NN分类、基于板的k-NN分类算法比较,体现了该分类方法的有效性。

In practical classification,the category of training samples is always fuzzy and uncertain which leads to difficult decision and affects the capability of classification.It applied the evidence theory and kernel function to k - nearest neighbor algorithm,introduced kernel distance of two samples which highlighted the feature differences;and learned parameter by adaptive method;exploited programming method to gain compatible probability of the category of samples and compared with other pignistic probability transforms,then made decision by compatible probability;it solves the problem of fuzzy and uncertain of training samples,and improves the accuracy of classification.Finally,compares the results with traditional k - nearest neighbor method,k - nearest neighbor method based on D - S theory,k - nearest neighbor method based on kernel distance,and proves the validity of our method.