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arXiv · 2401.06546

Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach

Vandad Imani·Elaheh Moradi·Carlos Sevilla-Salcedo·Vittorio Fortino·Jussi Tohka
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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.

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Vandad Imani, Elaheh Moradi, Carlos Sevilla-Salcedo, Vittorio Fortino, Jussi Tohka. 2024-01-12. Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach. https://arxiv.org/abs/2401.06546

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