School of Computing

Oct 9
14:00 - 15:00
compint: Tossapol Pomsuwan
Computational Intelligence Group Seminar
Feature Selection for the Classification of Longitudinal Human Ageing Data

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.

Location

SW101,
Cornwallis South West,
University of Kent,
Canterbury,
Kent,
CT2 7NF
United Kingdom
Map

Details

Contact: Marek Grzes
E: m.grzes@kent.ac.uk

School of Computing, University of Kent, Canterbury, Kent, CT2 7NF

Enquiries: +44 (0)1227 824180 or contact us.

Last Updated: 14/08/2015