A MODIFIED K-NEAREST NEIGHBOR CLASSIFIER TO DEAL WITH UNBALANCED CLASSES

被引：0
|
作者：
AlSukker, Akram [1 ]
Al-Ani, Ahmed [1 ]
Atiya, Amir [1 ]
机构：
[1] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
来源：
IJCCI 2009: PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE | 2009年
关键词：
kNN classifier; Unbalanced classes; Class-wise classification accuracy;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present in this paper a simple, yet valuable improvement to the traditional k-Nearest Neighbor (kNN) classifier. It aims at addressing the issue of unbalanced classes by maximizing the class-wise classification accuracy. The proposed classifier also gives the option of favoring a particular class through evaluating a small set of fuzzy rules. When tested on a number of UCI datasets, the proposed algorithm managed to achieve a uniformly good performance.
引用
收藏
页码：408 / +
页数：3
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