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001025669 0247_ $$2ISSN$$a1611-3349
001025669 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-03058
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001025669 1001_ $$0P:(DE-HGF)0$$aMiliou, Ioanna$$b0$$eEditor
001025669 1112_ $$a22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, Part II$$cStockholm$$d2024-04-24 - 2024-04-26$$wSweden
001025669 245__ $$aEmpirical Comparison Between Cross-Validation and Mutation-Validation in Model Selection
001025669 260__ $$aCham$$bSpringer Nature Switzerland$$c2024
001025669 29510 $$aAdvances in Intelligent Data Analysis XXII / Miliou, Ioanna (Editor) [https://orcid.org/0000-0002-1357-1967] ; Cham : Springer Nature Switzerland, 2024, Chapter 5 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-031-58555-5=978-3-031-58553-1 ; doi:10.1007/978-3-031-58553-1
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001025669 4900_ $$aLecture Notes in Computer Science$$v14642
001025669 520__ $$aMutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method. In this study, we empirically compared MV and k-fold CV using benchmark and real-world datasets. By employing Bayesian tests, we compared generalization estimates yielding three posterior probabilities: practical equivalence, CV superiority, and MV superiority. We also evaluated the differences in the capacity of the selected models and computational efficiency. We found that both MV and CV select models with practically equivalent generalization performance across various machine learning algorithms and the majority of benchmark datasets. MV exhibited advantages in terms of selecting simpler models and lower computational costs. However, in some cases MV selected overly simplistic models leading to underfitting and showed instability in hyperparameter selection. These limitations of MV became more evident in the evaluation of a real-world neuroscientific task of predicting sex at birth using brain functional connectivity.
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001025669 7001_ $$0P:(DE-HGF)0$$aPiatkowski, Nico$$b1$$eEditor
001025669 7001_ $$0P:(DE-HGF)0$$aPapapetrou, Panagiotis$$b2$$eEditor
001025669 7001_ $$0P:(DE-HGF)0$$aYu, Jinyang$$b3
001025669 7001_ $$0P:(DE-Juel1)184874$$aHamdan, Sami$$b4
001025669 7001_ $$0P:(DE-Juel1)190306$$aSasse, Leonard$$b5
001025669 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b6
001025669 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b7$$eCorresponding author
001025669 773__ $$a10.1007/978-3-031-58553-1_5
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001025669 9141_ $$y2024
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