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

arXiv: https://arxiv.org/abs/2401.06546 · HTML: https://mlsanity.com/paper/2401.06546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates](https://mlsanity.com/paper/1910.03231/) (2019)
- [Comparison of single and multitask learning for predicting cognitive decline based on MRI data](https://mlsanity.com/paper/2109.10266/) (2021)
- [Multi-task longitudinal forecasting with missing values on Alzheimer's Disease](https://mlsanity.com/paper/2201.05040/) (2022)
- [Multi-Objective Genetic Algorithm for Multi-View Feature Selection](https://mlsanity.com/paper/2305.18352/) (2023)

## Abstract (arXiv)

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.