Optimizing Feature Selection for Binary Classification with Noisy
  Labels: A Genetic Algorithm Approach
Vandad Imani Elaheh Moradi Carlos Sevilla-Salcedo Vittorio Fortino Jussi Tohka
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.