Saul, M.A. and Rostami, S., 2018. A Comparison of Re-sampling Techniques for Pattern Classification in Imbalanced Data-Sets. In: UKCI 2018 : 18th Annual UK Workshop on Computational Intelligence, 5-7 September 2018, Nottingham Trent University, Nottingham, United Kingdom.
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Official URL: http://ukci2018.uk/
Abstract
Class imbalance is a common challenge when dealing with pattern classification of real-world medical data-sets. An effective countermeasure typically used is a method known as re-sampling. In this paper we implement an ANN with different re-sampling techniques to subsequently compare and evaluate the performances. Re-sampling strategies included a control, under-sampling, over-sampling, and a combination of the two. We found that over-sampling and the combination of under- and over-sampling both led to a significantly superior classifier performance compared to under-sampling only in correctly predicting labelled classes.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | This paper is embargoed until after it has been presented at the conference. |
Uncontrolled Keywords: | machine learning; imbalanced data; over-sampling; undersampling; |
Group: | Faculty of Science & Technology |
ID Code: | 31059 |
Deposited By: | Symplectic RT2 |
Deposited On: | 26 Jul 2018 13:34 |
Last Modified: | 14 Mar 2022 14:12 |
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