Breast cancer detection using rank nearest neighbor classification rules

被引：48

作者：

Bagui, SC

Bagui, S

Pal, K

Pal, NR

机构：

[1] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700035, W Bengal, India

[2] Univ W Florida, Dept Math & Stat, Pensacola, FL 32514 USA

[3] Univ W Florida, Dept Comp Sci, Pensacola, FL 32514 USA

来源：

PATTERN RECOGNITION | 2003年 / 36卷 / 01期

关键词：

classification rules; rank nearest neighbor rules; nearest neighbor rules; breast masses; breast cancer detection; cell nucleus; mean texture; worst mean area; error rate; Bayes error rate;

D O I：

10.1016/S0031-3203(02)00044-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this article, we propose a new generalization of the rank nearest neighbor (RNN) rule for multivariate data for diagnosis of breast cancer. We study the performance of this rule using two well known databases and compare the results with the conventional k-NN rule. We observe that this rule performed remarkably well, and the computational complexity of the proposed k-RNN is much less than the conventional k-NN rule. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

引用

页码：25 / 34

页数：10

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