A new nearest-neighbor rule in the pattern classification problem

被引：29

作者：

Hattori, K ^{[1
]}

Takahashi, M ^{[1
]}

机构：

[1] Toyohashi Univ Technol, Dept Elect & Elect Engn, Toyohashi, Aichi 441, Japan

来源：

PATTERN RECOGNITION | 1999年 / 32卷 / 03期

关键词：

nearest-neighbor rule; distance-weighted k-nearest neighbor rule; fuzzy k-nearest neighbor rule; leave one out technique; number of samples misclassified; pattern classification problems;

D O I：

10.1016/S0031-3203(98)00097-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new nearest-neighbor (NN) rule is proposed. In this rule, the ii-nearest neighbors of an input sample are obtained in each class. Two classification examples are presented to lest the NN rule proposed. The number of samples misclassified N-m is evaluated. The minimum of N-m in the the NN rule proposed is found to be nearly equal to or less than those in the k-NN, distance-weighted k-NN and fuzzy k-NN rules. The NN rule proposed is shown to be very flexible. It will yield good classification results, if the parameters introduced in it are optimized. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

引用

页码：425 / 432

页数：8

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