Evaluation of sampling methods for learning from imbalanced data

Goel, G; Maguire, L; Li, Y; McLoone, S

Abstract

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.

Citation

Goel, G., Maguire, L., Li, Y., & McLoone, S. (2013). Evaluation of sampling methods for learning from imbalanced data. In D. Huang, V. Bevilacqua, J. Figueroa, & P. Premaratne (Eds.), Intelligent Computing Theories : 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013, Proceedings (392-401). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_47

• Publication Date: Jan 1, 2013
• Deposit Date: Jul 8, 2015
• Pages: 392-401
• Series Title: Lecture Notes in Computer Science
• Series Number: 7995
• Book Title: Intelligent Computing Theories : 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013, Proceedings
• ISBN: 9783642394782
• DOI: https://doi.org/10.1007/978-3-642-39479-9_47
• Publisher URL: http://dx.doi.org/10.1007/978-3-642-39479-9_47
• Related Public URLs: http://link.springer.com/book/10.1007/978-3-642-39479-9