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GA-Auto-PU: A genetic algorithm-based automated machine learning system for Positive-Unlabeled learning

Saunders, Jack, Freitas, Alex A. (2022) GA-Auto-PU: A genetic algorithm-based automated machine learning system for Positive-Unlabeled learning. In: Proceedings of the GECCO’22 Companion (Genetic and Evolutionary Computation Conference). . pp. 288-291. ACM Press ISBN 978-1-4503-9268-6. (doi:10.1145/3520304.3528932) (KAR id:95803)

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Language: English

DOI for this version: 10.1145/3520304.3528932

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Official URL:
https://doi.org/10.1145/3520304.3528932

Abstract

Positive-Unlabeled (PU) learning is a growing field of machine learning that now consists of numerous algorithms; the number is now so large that considering an extensive manual search to select the best algorithm for a given task is impractical. As such, the area of PU learning could benefit from an Automated Machine Learning (Auto-ML) system, which selects the best algorithm for a given input dataset, among a pre-defined set of candidate algorithms. This work proposes such with GA-Auto-PU, a Genetic Algorithm-based Auto-ML system that can generate PU learning algorithms. Experiments with 20 real-world datasets show that GA-Auto-PU

significantly outperformed a state-of-the-art PU learning method.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/3520304.3528932
Additional information: For the purpose of open access, the author has applied a CC BY public copyright licence (where permitted by UKRI, an Open Government Licence or CC BY ND public copyright licence may be used instead) to any Author Accepted Manuscript version arising. 18/07/2022
Uncontrolled keywords: Genetic algorithms, Machine Learning, Auto-ML, Classification
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: [211] U.K. Engineering and physical Sciences Research Council (EPSRC)
Depositing User: Alex Freitas
Date Deposited: 14 Jul 2022 09:35 UTC
Last Modified: 21 Jul 2022 14:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/95803 (The current URI for this page, for reference purposes)
Saunders, Jack: https://orcid.org/0000-0002-0801-2909
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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