Résumé:
Feature selection is a crucial step in the machine learning process, aiming to identify and
select the most informative features from an original dataset. Among the techniques used in
this process, we find MI, CH2, IGI, etc. Despite their effectiveness, these techniques suffer
from the drawback of redundancy, resulting in poor classification model performance.
In this work, we opted for a hybrid approach based on the IG method and genetic algorithm.
Firstly, we use IG to evaluate the relationship between each feature and the class variable.
Features with high IG scores are considered more discriminative. Then, we employ a genetic
algorithm to search within the space of features selected by IG and find an optimal subset
using operations such as selection, crossover, and mutation.
The experimental results confirm that our hybrid method has achieved our objective by
improving performance, significantly reducing redundancy, and outperforming other methods.
