Feature Selection for the Classification of Longitudinal Human Ageing Data

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.