Extending Bagging for Imbalanced Data

被引：23

作者：

Blaszczynski, Jerzy ^{[1
]}

Stefanowski, Jerzy ^{[1
]}

Idkowiak, Lukasz ^{[1
]}

机构：

[1] Poznan Univ Tech, Inst Comp Sci, PL-60965 Poznan, Poland

来源：

PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS CORES 2013 | 2013年 / 226卷

关键词：

Class distributions - Experimental comparison - Imbalanced data - Neighbourhood - Over sampling - Under-sampling;

D O I：

10.1007/978-3-319-00969-8_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside its neighbourhood. Experiments indicate that this proposal is competitive to best undersampling bagging extensions.

引用

页码：269 / 278

页数：10

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