Evaluation Measures of the Classification Performance of Imbalanced Data Sets

被引：185

作者：

Gu, Qiong ^{[1
,2
]}

Zhu, Li ^{[2
]}

Cai, Zhihua ^{[2
]}

机构：

[1] Xiangfan Univ, Fac Math & Comp Sci, Xiangfan 441053, Hubei, Peoples R China

[2] China Univ Geosci, Sch Comp, Wuhan 430074, Peoples R China

来源：

COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS | 2009年 / 51卷

关键词：

Evaluation; classification performance; imbalanced data sets;

D O I：

10.1007/978-3-642-04962-0_53

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Discriminant Measures for Classification Performance play a critical role in guiding the design of classifiers, assessment methods and evaluation measures are at least as important as algorithm and are the first key stage to a successful data mining. We systematically summarized the evaluation measures of Imbalanced Data Sets (IDS). Several different type measures, such as commonly performance evaluation measures and visualizing classifier performance measures have been analyzed and compared. The problems of these measures towards IDS may lead to misunderstanding of classification results and even wrong strategy decision. Beside that, a series of complex numerical evaluation measures were also investigated which can also serve for evaluating classification performance of IDS.

引用

页码：461 / +

页数：2

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