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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)

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https://doi.org/10.1109/ICDMW.2017.102

Abstract

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: Faculties > Sciences > School of Computing
Depositing User: T. Pomsuwan
Date Deposited: 18 Apr 2018 10:45 UTC
Last Modified: 29 May 2019 20:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/66776 (The current URI for this page, for reference purposes)
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