A New Approach to Determine the Similarity Parameters in Evidence-Theoretic k-NN Rule

LIU Ming; YUAN Bao-zong; TANG Xiao-fang

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A New Approach to Determine the Similarity Parameters in Evidence-Theoretic k-NN Rule

更新时间：2025-07-16

- A New Approach to Determine the Similarity Parameters in Evidence-Theoretic k-NN Rule
- Acta Electronica Sinica Vol. 33, Issue 4, Pages: 766-768(2005)
- 作者机构：
  
  1. 北京交通大学信息科学研究所,北京,100044
  2. 河北大学电子信息工程学院,河北,保定,071002
  3. 北京交通大学信息科学研究所北京,100044
  4. 河北大学电子信息工程学院河北保定,071002
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.73
- Published Online：25 April 2005，
  
  Published：2005
- 稿件说明：
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LIU Ming, YUAN Bao-zong, TANG Xiao-fang. A New Approach to Determine the Similarity Parameters in Evidence-Theoretic k-NN Rule[J]. Acta Electronica Sinica, 2005, 33(4): 766-768.
DOI：

LIU Ming, YUAN Bao-zong, TANG Xiao-fang. A New Approach to Determine the Similarity Parameters in Evidence-Theoretic k-NN Rule[J]. Acta Electronica Sinica, 2005, 33(4): 766-768. DOI：

摘要

本文提出了一种确定证据理论k-NN分类规则中相似度参数的新方法.对于一个模式识别问题

我们首先为每一模式类求得一个参考最近邻距离

使其在最小错误率意义下将训练样本集中属于该模式类的样本与其他样本分离

然后根据所得参考最近邻距离计算相似度函数参数.该方法在训练集比较小、样本非高斯分布条件下仍然能够计算出比较准确的参数

使得相应的分类错误率较小

而且时间复杂度比L.M.Zouhal的方法低约4-8倍.

Abstract

This paper presents a new approach to determine the similarity parameters in the Evidence-Theoretic k-NN Classification Rule. Given a pattern recognition problem

we first compute a reference nearest neighbor distance to separate samples of one class from other samples with least error rate

and then calculate the similarity parameters based on the obtained distance. Under the condition of small scale samples with non-gaussian distribution

the proposed method can get more suitable parameters and thus reduce classification error rate.Furthermore

its computation complexity is 4-8 times lower than that of L.M.Zouhal's method.

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