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Feature Selection for the Classification of Longitudinal Human Ageing Data

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

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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