TY  - CONF
AU  - Yu, Jinyang
AU  - Hamdan, Sami
AU  - Sasse, Leonard
AU  - Morrison, Abigail
AU  - Patil, Kaustubh R.
A3  - Miliou, Ioanna
A3  - Piatkowski, Nico
A3  - Papapetrou, Panagiotis
TI  - Empirical Comparison Between Cross-Validation and Mutation-Validation in Model Selection
VL  - 14642
CY  - Cham
PB  - Springer Nature Switzerland
M1  - FZJ-2024-03058
SN  - 978-3-031-58555-5 (print)
T2  - Lecture Notes in Computer Science
SP  - 56 - 67
PY  - 2024
AB  - Mutation 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.
T2  - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, Part II
CY  - 24 Apr 2024 - 26 Apr 2024, Stockholm (Sweden)
Y2  - 24 Apr 2024 - 26 Apr 2024
M2  - Stockholm, Sweden
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:001295920900005
DO  - DOI:10.1007/978-3-031-58553-1_5
UR  - https://juser.fz-juelich.de/record/1025669
ER  -