Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911182745501696 |
|---|---|
| author | Imani, Vandad Moradi, Elaheh Sevilla-Salcedo, Carlos Fortino, Vittorio Tohka, Jussi |
| author_facet | Imani, Vandad Moradi, Elaheh Sevilla-Salcedo, Carlos Fortino, Vittorio Tohka, Jussi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach Imani, Vandad Moradi, Elaheh Sevilla-Salcedo, Carlos Fortino, Vittorio Tohka, Jussi Machine Learning Computer Vision and Pattern Recognition Neural and Evolutionary Computing 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. |
| title | Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach |
| topic | Machine Learning Computer Vision and Pattern Recognition Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.06546 |