Balanced Neighborhood Classifiers for Imbalanced Data Sets

被引：0

作者：

Zhu, Shunzhi ^{[1
]}

Ma, Ying ^{[1
]}

Pan, Weiwei ^{[1
]}

Zhu, Xiatian ^{[2
]}

Luo, Guangchun ^{[3
]}

机构：

[1] Xiamen Univ Technol, Xiamen, Peoples R China

[2] Queen Mary Univ London, London E1 4NS, England

[3] Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2014年 / E97D卷 / 12期

基金：

中国国家自然科学基金;

关键词：

machine learning; class imbalance; class distribution; classification; ALGORITHMS;

D O I：

10.1587/transinf.2014EDL8064

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A Balanced Neighborhood Classifier (BNEC) is proposed for class imbalanced data. This method is not only well positioned to capture the class distribution information, but also has the good merits of high-fitting-performance and simplicity. Experiments on both synthetic and real data sets show its effectiveness.

引用

页码：3226 / 3229

页数：4

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