Learning from imbalanced sets through resampling and weighting

被引：0

作者：

Barandela, R

Sánchez, JS

García, V

Ferri, FJ

机构：

[1] Inst Tecnol Toluca, Metepec 52140, Mexico

[2] Dept Llenguatges Sistemas Informat, Castellon de La Plana 12071, Spain

[3] Univ Valencia, Dept Informat, E-46100 Burjassot, Spain

[4] Inst Geog, Havana, Cuba

来源：

PATTERN RECOGNITION AND IMAGE ANALYSIS, PROCEEDINGS | 2003年 / 2652卷

关键词：

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：

The problem of imbalanced training sets in supervised pattern recognition methods is receiving growing attention. Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. It has been observed that this situation, which arises in several practical situations, may produce an important deterioration of the classification accuracy, in particular with patterns belonging to the less represented classes. In the present paper, we introduce a new approach to design an instance-based classifier in such imbalanced environments.

引用

页码：80 / 88

页数：9

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