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Evaluation of sampling methods for learning from imbalanced data

Goel, Garima, Maguire, Liam, Li, Yuhua ORCID: and McLoone, Sean 2013. Evaluation of sampling methods for learning from imbalanced data. Presented at: ICIC 2013, Nanning, China, 28-31 Jul 2013. Published in: Huang, De-Shuang, Bevilacqua, Vitoantonio, Figueroa, Juan Carlos and Premaratne, Prashan eds. Intelligent Computing Theories. Lecture Notes in Computer Science. Information Systems and Applications, incl. Internet/Web, and HCI , vol.7995 Berlin, Heidelberg: Springer, pp. 392-401. 10.1007/978-3-642-39479-9_47

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The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer
ISBN: 978-3-642-39478-2
ISSN: 1611-3349
Last Modified: 07 Nov 2022 09:26

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