Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach
Machine Learning
2025-09-30 v1 Computer Vision and Pattern Recognition
Neural and Evolutionary Computing
Abstract
Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Feature Selection Genetic Algorithm (NMFS-GA), for selecting optimal feature subsets in binary classification with noisy labels. NMFS-GA offers a unified framework for selecting feature subsets that are both accurate and interpretable. We evaluate NMFS-GA on synthetic datasets with label noise, a Breast Cancer dataset enriched with noisy features, and a real-world ADNI dataset for dementia conversion prediction. Our results indicate that NMFS-GA can effectively select feature subsets that improve the accuracy and interpretability of binary classifiers in scenarios with noisy labels.
Cite
@article{arxiv.2401.06546,
title = {Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach},
author = {Vandad Imani and Elaheh Moradi and Carlos Sevilla-Salcedo and Vittorio Fortino and Jussi Tohka},
journal= {arXiv preprint arXiv:2401.06546},
year = {2025}
}