Genetic algorithm feature selection resilient to increasing amounts of data imputation

Abstract

This paper investigates the robustness of a genetic algorithm (GA) in feature selection across a dataset with increasing imputed missing values.
Feature selection can be beneficial in predictive modeling to reduce computational costs and potentially improve performance. Beyond these benefits, it also enables a clearer understanding of the algorithm's decision-making processes. In the context of real-world datasets that can contain missing values, feature selection becomes more challenging.
A robust feature selection algorithm should be able to identify the key features despite missing data values.
We investigate the effectiveness of this approach against two other feature selection algorithms on a dataset with increasingly imputed values to determine whether it can sustain good performance with only the selected features.
Our results reveal that compared to the other two methods, the features selected by GA resulted in better classification performance across different imputation rates and methods.