A comparison of re-sampling techniques for pattern classification in imbalanced data-sets
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Journal: Advances in Intelligent Systems and Computing
Class imbalance is a common challenge when dealing with pattern classification of real-world medical data-sets. An effective counter-measure 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.
This data was imported from Web of Science (Lite):
Journal: ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI)