Pomsuwan, Tossapol, Freitas, Alex A. (2017) Feature Selection for the Classification of Longitudinal Human Ageing Data. In: 2017 IEEE International Conference on Data Mining Workshops. . pp. 739-746. IEEE, USA ISBN 978-1-5386-1480-8. E-ISBN 978-1-5386-3800-2. (doi:10.1109/ICDMW.2017.102) (KAR id:66776)
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| Official URL https://doi.org/10.1109/ICDMW.2017.102 |
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Abstract
We propose a new variant of the Correlation-based
where variables are repeatedly measured across different time
created using data from the English Longitudinal Study of Ageing
variables to be predicted. The results show that, overall, the
the standard CFS and the baseline approach of no feature
as classification algorithms (although the difference in
the most relevant features selected by J48 across the datasets.
| Item Type: | Conference or workshop item (Paper) |
|---|---|
| DOI/Identification number: | 10.1109/ICDMW.2017.102 |
| Uncontrolled keywords: | classification, feature selection, longitudinal data,age-related diseases |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | T. Pomsuwan |
| Date Deposited: | 18 Apr 2018 10:45 UTC |
| Last Modified: | 16 Feb 2021 13:54 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/66776 (The current URI for this page, for reference purposes) |
| Freitas, Alex A.: |  https://orcid.org/0000-0001-9825-4700 |
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