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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)


We propose a new variant of the Correlation-based

Feature Selection (CFS) method for coping with longitudinal data

where variables are repeatedly measured across different time

points. The proposed CFS variant is evaluated on ten datasets

created using data from the English Longitudinal Study of Ageing

(ELSA), with different age-related diseases used as the class

variables to be predicted. The results show that, overall, the

proposed CFS variant leads to better predictive performance than

the standard CFS and the baseline approach of no feature

selection, when using Naïve Bayes and J48 decision tree induction

as classification algorithms (although the difference in

performance is very small in the results for J4.8). We also report

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: (The current URI for this page, for reference purposes)

University of Kent Author Information

Pomsuwan, Tossapol.

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CReDIT Contributor Roles:

Freitas, Alex A..

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