A quick evidential classification algorithm based on k-nearest neighbor rule
A quick evidential classification algorithm based on k-nearest neighbor rule
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DOI：
10.1109/icmlc.2003.1260141
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发表时间：
2003-11
期刊：
影响因子：
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通讯作者：
Zhuang Wang;Weidong Hu;Wenxian Yu
中科院分区：
文献类型：
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作者：
Zhuang Wang;Weidong Hu;Wenxian Yu
在Dempster-Shafer证据理论框架下，针对少数证据来源不确定的情况，根据证据之间的冲突，构造了一个描述证据之间相似性的距离函数。为了克服传统快速k-近邻证据分类算法的这些缺点，提出了一种快速k-近邻证据分类算法--超级球搜索证据分类算法。S-BSEC）算法。仿真结果表明，在相同的识别率和k值下，该方法的识别速度上级传统的k-NN算法，且超球算法对训练样本的搜索顺序不敏感。
Under the frame of Dempster-Shafer theory of evidence, a distance function to depict comparability between evidences is constructed according to the conflict among evidences, which is for the case that the origin of few evidences is uncertain. In order to conquer these disadvantages of traditional quick k-nearest neighbor (k-NN) classification algorithm, this paper proposes a quick k-NN evidence classification algorithm-super-ball search evidence classification (ab. S-BSEC) algorithm based on near neighbor searching. Simulation results show that this method is superior to the traditional k-NN algorithm in terms of the recognition speed under the same recognition rate and k, and super-ball algorithm is not sensitive to searching order of training sample.