A comparison of re-sampling techniques for pattern classification in imbalanced data-sets
Authors: Saul, M.A. and Rostami, S.
Journal: Advances in Intelligent Systems and Computing
Volume: 840
Pages: 240-251
ISBN: 9783319979816
ISSN: 2194-5357
DOI: 10.1007/978-3-319-97982-3_20
Abstract: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.
https://eprints.bournemouth.ac.uk/31059/
Source: Scopus
A Comparison of Re-sampling Techniques for Pattern Classification in Imbalanced Data-Sets
Authors: Saul, M.A. and Rostami, S.
Journal: ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI)
Volume: 840
Pages: 240-251
eISSN: 2194-5365
ISBN: 978-3-319-97981-6
ISSN: 2194-5357
DOI: 10.1007/978-3-319-97982-3_20
https://eprints.bournemouth.ac.uk/31059/
Source: Web of Science (Lite)
A Comparison of Re-sampling Techniques for Pattern Classification in Imbalanced Data-Sets
Authors: Saul, M.A. and Rostami, S.
Conference: UKCI 2018 : 18TH ANNUAL UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE
Dates: 5-7 September 2018
https://eprints.bournemouth.ac.uk/31059/
Source: Manual
A Comparison of Re-sampling Techniques for Pattern Classification in Imbalanced Data-Sets
Authors: Saul, M.A. and Rostami, S.
Conference: UKCI 2018 : 18th Annual UK Workshop on Computational Intelligence
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.
https://eprints.bournemouth.ac.uk/31059/
Source: BURO EPrints