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
收藏
页码:461 / +
页数:2
相关论文
共 50 条
[41]   Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network [J].
Du, Chaofan ;
Liu, Peter Xiaoping ;
Zheng, Minhua .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 214
[42]   FIAO: Feature Information Aggregation Oversampling for imbalanced data classification [J].
Wang, Fei ;
Zheng, Ming ;
Hu, Xiaowen ;
Li, Hongchao ;
Wang, Taochun ;
Chen, Fulong .
APPLIED SOFT COMPUTING, 2024, 161
[43]   RSMOTE: improving classification performance over imbalanced medical datasets [J].
Naseriparsa, Mehdi ;
Al-Shammari, Ahmed ;
Sheng, Ming ;
Zhang, Yong ;
Zhou, Rui .
HEALTH INFORMATION SCIENCE AND SYSTEMS, 2020, 8 (01)
[44]   An approach for classification of highly imbalanced data using weighting and undersampling [J].
Anand, Ashish ;
Pugalenthi, Ganesan ;
Fogel, Gary B. ;
Suganthan, P. N. .
AMINO ACIDS, 2010, 39 (05) :1385-1391
[45]   Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity [J].
Sankalp Jain ;
Eleni Kotsampasakou ;
Gerhard F. Ecker .
Journal of Computer-Aided Molecular Design, 2018, 32 :583-590
[46]   A Submodular Optimization Framework for Imbalanced Text Classification With Data Augmentation [J].
Alemayehu, Eyor ;
Fang, Yi .
IEEE ACCESS, 2023, 11 :41680-41696
[47]   An improved weighted extreme learning machine for imbalanced data classification [J].
Chengbo Lu ;
Haifeng Ke ;
Gaoyan Zhang ;
Ying Mei ;
Huihui Xu .
Memetic Computing, 2019, 11 :27-34
[48]   Comparing the performance of meta-classifiers-a case study on selected imbalanced data sets relevant for prediction of liver toxicity [J].
Jain, Sankalp ;
Kotsampasakou, Eleni ;
Ecker, Gerhard F. .
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2018, 32 (05) :583-590
[49]   RSMOTE: improving classification performance over imbalanced medical datasets [J].
Mehdi Naseriparsa ;
Ahmed Al-Shammari ;
Ming Sheng ;
Yong Zhang ;
Rui Zhou .
Health Information Science and Systems, 8
[50]   A Novel Weighted Ensemble Method to Overcome the Impact of Under-fitting and Over-fitting on the Classification Accuracy of the Imbalanced Data Sets [J].
Fatima, Ghulam ;
Saeed, Sana .
PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH, 2021, 17 (02) :483-496