Provost, Simon, Freitas, Alex A. (2024) Auto-sklong: a new AutoML system for longitudinal classification. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine. . pp. 3645-3650. IEEE ISBN 979-8-3503-8622-6. (doi:10.1109/bibm62325.2024.10821737) (KAR id:108468)
PDF
Author's Accepted Manuscript
Language: English |
|
Download this file (PDF/12MB) |
Preview |
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: https://doi.org/10.1109/bibm62325.2024.10821737 |
Abstract
Automated Machine Learning (AutoML) addresses the challenge of selecting the best machine learning algorithm and its hyperparameter settings for a given dataset. However, existing AutoML systems typically focus on standard classification tasks and cannot directly exploit temporal information e.g. in longitudinal datasets, which contain multiple measurements of the same features over time — a common scenario in biomedical applications. We introduce Auto-Sklong, the first AutoML system that includes longitudinal classification algorithms in its search space. Experiments with 20 age-related disease datasets from the English Longitudinal Study of Ageing demonstrate that Auto-Sklong significantly outperforms a state-of-the-art AutoML system (Auto-Sklearn) and two baseline random forest methods in terms of predictive accuracy.
Item Type: | Conference or workshop item (Paper) |
---|---|
DOI/Identification number: | 10.1109/bibm62325.2024.10821737 |
Additional information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Uncontrolled keywords: | radio frequency; metalearning; machine learning algorithms; databases; automated machine learning; time measurement; classification algorithms; standards; random forests; diseases |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 23 Jan 2025 14:52 UTC |
Last Modified: | 24 Jan 2025 15:11 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/108468 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):