Rapid Selection of Machine Learning Models Using Greedy Cross Validation

dc.contributor.author Soper, Daniel
dc.date.accessioned 2021-12-24T18:28:43Z
dc.date.available 2021-12-24T18:28:43Z
dc.date.issued 2022-01-04
dc.description.abstract This paper introduces a greedy method of performing k-fold cross validation and shows how the proposed greedy method can be used to rapidly identify optimal or near-optimal machine learning (ML) models. Although many methods have been proposed that apply metaheuristic and other search methods to the hyperparameter space as a means of accelerating ML model selection, the cross-validation process itself has been overlooked as a means of rapidly identifying optimal ML models. The current study remedies this oversight by describing a simple, greedy cross validation algorithm and demonstrating that even in its simplest form, the greedy cross validation method can vastly reduce the average time required to identify an optimal or near-optimal ML model within a large set of candidate models. This substantially reduced search time is shown to hold across a variety of different ML algorithms and real-world datasets.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.903
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/80245
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Computational Intelligence and State-of-the-Art Data Analytics
dc.subject greedy cross validation
dc.subject hyperparameter optimization
dc.subject machine learning
dc.subject model selection
dc.title Rapid Selection of Machine Learning Models Using Greedy Cross Validation
dc.type.dcmi text
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