Skip to main content
Kent Academic Repository

Auto-sklong: a new AutoML system for longitudinal classification

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)

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)

University of Kent Author Information

  • Depositors only (login required):

Total unique views of this page since July 2020. For more details click on the image.