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dc.contributor.authorSu, CTen_US
dc.contributor.authorChen, LSen_US
dc.contributor.authorYih, YWen_US
dc.date.accessioned2014-12-08T15:15:41Z-
dc.date.available2014-12-08T15:15:41Z-
dc.date.issued2006-10-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2005.09.082en_US
dc.identifier.urihttp://hdl.handle.net/11536/11716-
dc.description.abstractWhen learning from imbalanced/skewed data, which almost all the instances are labeled as one class while far few instances are labeled as the other class, traditional machine learning algorithms tend to produce high accuracy over the majority class but poor predictive accuracy over the minority class. This paper proposes a novel method called 'knowledge acquisition via information granulation' (KAIG) model which not only can remove some unnecessary details and provide a better insight into the essence of data but also effectively solve 'class imbalance' problems. In this model, the homogeneity index (H-index) and the undistinguishable ratio (U-ratio) are successfully introduced to determine a suitable level of granularity. We also developed the concept of sub-attributes to describe granules and tackle the overlapping among granules. Seven data sets from UCI data bank, including one imbalanced diagnosis data (pima-Indians-diabetes), are provided to evaluate the effectiveness of KAIG model. By using different performance indexes, overall accuracy, G-mean and Receiver Operation Characteristic (ROC) curve, the experimental results comparing with C4.5 and Support Vector Machine (SVM) demonstrate the superiority of our method. (c) 2005 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectinformation granulationen_US
dc.subjectfuzzy ARTen_US
dc.subjectgranular computingen_US
dc.subjectknowledge acquisitionen_US
dc.subjectimbalanced dataen_US
dc.titleKnowledge acquisition through information granulation for imbalanced dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2005.09.082en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume31en_US
dc.citation.issue3en_US
dc.citation.spage531en_US
dc.citation.epage541en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000238750200009-
dc.citation.woscount33-
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