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 it also adds an important contribution to the explainability aspects of the results. An evolution strategy for feature selection is proposed in this paper. Feature weights are evolved with decision trees that use the Nash equilibrium concept to split node data. Trees are maintained until the variation in probabilities induced by feature weights stagnates. Predictions are made based on the information provided by all the trees. Numerical experiments illustrate the performance of the approach compared to other classification methods.
Citare
@Inproceedings{Lung2023AnEA,
author = {R. Lung and M. Suciu},
booktitle = {International Conference on Machine Learning, Optimization, and Data Science},
title = {An Evolutionary Approach to Feature Selection and Classification},
year = {2023}
}