A new nearest neighbor classifier based on multi-harmonic mean distances

机译：基于多谐平均距离的新近邻分类器

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K-nearest neighbor (KNN) rule is a simple and effective classifier in pattern recognition. In this paper, we propose a new nearest neighbor classifier based on multi-harmonic mean distances, in order to overcome the sensitivity of the neighborhood size k and improve the classification performance. The proposed method is called a harmonic mean distance-based k-nearest neighbor classifier (HMDKNN). It mainly designs the multi-harmonic mean distances based on the multi-local mean vectors calculated by utilizing k nearest neighbors of the given query sample in each class. Using the multi-harmonic mean distances per class, a new nested harmonic mean distance in each class is designed as the classification decision and the query sample is classified into the class with the closest nested harmonic mean distance among all classes. The experimental results on the UCI data sets show that the proposed HMDKNN performs better with the less sensitiveness to k, compared to the state-of-art KNN-based methods.

机译：K最近邻（KNN）规则是模式识别中一种简单有效的分类器。在本文中，我们提出了一种基于多重谐波平均距离的新近邻分类器，以克服邻域大小k的敏感性，提高分类性能。所提出的方法称为基于谐波平均距离的k最近邻分类器（HMDKNN）。它主要基于通过利用每个类中给定查询样本的k个最近邻居计算出的多局部平均向量来设计多谐平均距离。使用每个类别的多谐波平均距离，将每个类别中的新嵌套谐波平均距离设计为分类决策，并将查询样本分类为所有类别中嵌套谐波平均距离最近的类别。在UCI数据集上的实验结果表明，与最新的基于KNN的方法相比，所提出的HMDKNN对k的敏感度更低。

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来源
《2017 International Conference on Security, Pattern Analysis, and Cybernetics》|2017年|31-36|共6页
会议地点 Shenzhen(CN)
作者
Hongxing Ma; Jianping Gou; Weihua Ou; Shaoning Zeng; Yunbo Rao; Hebiao Yang;
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作者单位

College of Computer Science, Shaanxi, Normal University, Xi'an, Shaanxi, 710119;

School of Computer Science and Telecommunication Engineering, Jiangsu University, ZhenJiang, Jiangsu, 212013, P. R. China;

School of Big Data and Computer, Science, Guizhou Normal University, Guiyang, Guizhou, 550025, P. R. China;

School of Information Science and Technology, Huizhou University, Huizhou, Guangdong, 516007, P. R. China;

School of Information and Software, Engineering, University of Electronic, Science and Technology of China, Chengdu, Sichuan, 610054, P.R China;

School of Computer Science and Telecommunication Engineering, Jiangsu University, ZhenJiang, Jiangsu, 212013, P. R. China;

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正文语种 eng
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关键词
Harmonic analysis; Sensitivity; Electronic mail; Computer science; Training; Security; Pattern analysis;

机译：谐波分析灵敏度电子邮件计算机科学培训安全性模式分析;

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A new nearest neighbor classifier based on multi-harmonic mean distances

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