Abstract
The feature selection problem has become a key undertaking within machine learning. For classification problems, it is known to reduce the computational complexity of parameter estimation, but also it adds an important contribution to the explainability aspects of the results. In this paper, a genetic algorithm for feature selection is proposed. The importance, as well as the effectiveness of features selected by each individual, is evaluated by using decision trees. The feature importance indicated by the decision tree is used during selection and recombination. The tree inducted by the best individual in the population is used for classification. Numerical experiments illustrate the behavior of the approach.
Citare
@Inproceedings{Suciu2023FeatureSB,
author = {M. Suciu and R. Lung},
booktitle = {Hybrid Artificial Intelligence Systems},
title = {Feature Selection Based on a Decision Tree Genetic Algorithm},
year = {2023}
}