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dc.contributor.authorVuttipittayamongkol, Pattaramon
dc.contributor.authorElyan, Eyad
dc.contributor.authorPetrovski, Andrei
dc.contributor.authorJayne, Chrisina
dc.date.accessioned2019-02-08T09:11:43Z
dc.date.available2019-02-08T09:11:43Z
dc.date.issued2018-11-09en
dc.identifier.citationVUTTIPITTAYAMONGKOL, P., ELYAN, E., PETROVSKI, A. and JAYNE, C. 2018. Overlap-based undersampling for improving imbalanced data classification. In Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A. (eds.) Intelligent data engineering and automated learning: proceedings of the 19th International intelligent data engineering and automated learning conference (IDEAL 2018), 21-23 November 2018, Madrid, Spain. Lecture notes in computer science, 11341. Cham: Springer [online], pages 689-697. Available from: https://doi.org/10.1007/978-3-030-03493-1_72en
dc.identifier.isbn9783030034924en
dc.identifier.isbn9783030034931en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/10059/3281
dc.description.abstractClassification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling methods are widely used due to their simplicity and flexibility. Most existing resampling techniques aim at rebalancing class distribution. However, class imbalance is not the only factor that impacts the performance of the learning algorithm. Class overlap has proved to have a higher impact on the classification of imbalanced datasets than the dominance of the negative class. In this paper, we propose a new undersampling method that eliminates negative instances from the overlapping region and hence improves the visibility of the minority instances. Testing and evaluating the proposed method using 36 public imbalanced datasets showed statistically significant improvements in classification performance.en
dc.language.isoengen
dc.publisherSpringeren
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0en
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectUndersamplingen
dc.subjectOverlapen
dc.subjectImbalanced dataen
dc.subjectClassificationen
dc.subjectFuzzy C-meansen
dc.subjectResamplingen
dc.titleOverlap-based undersampling for improving imbalanced data classification.en
dc.typeConference publicationsen
dc.publisher.urihttps://doi.org/10.1007/978-3-030-03493-1_72en
dcterms.publicationdate2018-12-21en
refterms.accessExceptionNAen
refterms.depositExceptionNAen
refterms.panelBen
refterms.technicalExceptionNAen
refterms.versionAMen
rioxxterms.publicationdate2018-11-09en
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.versionAMen


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