标题:Combining integrated sampling with SVM ensembles for learning from imbalanced datasets
作者:Liu, Y.;Yu, X.;Huang, J.X.;An, A.
作者机构:[Liu, Y] School of Computer Science and Technology, Shandong University, Jinan, China;[ Yu, X] School of Computer Science and Technology, Shandong Uni 更多
通讯作者:Huang, J X
通讯作者地址:[Huang, JX]York Univ, Sch Informat Technol, Toronto, ON M3J 2R7, Canada.
来源:Information Processing & Management: Libraries and Information Retrieval Systems and Communication Networks: An International Journal
出版年:2011
卷:47
期:4
页码:617-631
DOI:10.1016/j.ipm.2010.11.007
关键词:Classification;Data sampling;Imbalanced data mining
摘要:Learning from imbalanced datasets is difficult. The insufficient information that is associated with the minority class impedes making a clear understanding of the inherent structure of the dataset. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced, because they aim to optimize the overall accuracy without considering the relative distribution of each class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs may suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed. We propose to combine an integrated sampling technique, which incorporates both over-sampling and under-sampling, with an ensemble of SVMs to improve the prediction performance. Extensive experiments show that our method outperforms individual SVMs as well as several other state-of-the-art classifiers.
收录类别:EI;SCOPUS;SCIE;SSCI
WOS核心被引频次:47
Scopus被引频次:65
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957591079&doi=10.1016%2fj.ipm.2010.11.007&partnerID=40&md5=85b6c207b502db3864e502fb075d42e0
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