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@INPROCEEDINGS{Miliou:1025669,
      author       = {Yu, Jinyang and Hamdan, Sami and Sasse, Leonard and
                      Morrison, Abigail and Patil, Kaustubh R.},
      editor       = {Miliou, Ioanna and Piatkowski, Nico and Papapetrou,
                      Panagiotis},
      title        = {{E}mpirical {C}omparison {B}etween {C}ross-{V}alidation
                      and {M}utation-{V}alidation in {M}odel {S}election},
      volume       = {14642},
      address      = {Cham},
      publisher    = {Springer Nature Switzerland},
      reportid     = {FZJ-2024-03058},
      isbn         = {978-3-031-58555-5 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {56 - 67},
      year         = {2024},
      comment      = {Advances 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},
      booktitle     = {Advances 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},
      abstract     = {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.},
      month         = {Apr},
      date          = {2024-04-24},
      organization  = {22nd International Symposium on
                       Intelligent Data Analysis, IDA 2024,
                       Proceedings, Part II, Stockholm
                       (Sweden), 24 Apr 2024 - 26 Apr 2024},
      cin          = {INM-7 / IAS-6},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5253 - Neuroimaging (POF4-525) / 5254 - Neuroscientific
                      Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253 / G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001295920900005},
      doi          = {10.1007/978-3-031-58553-1_5},
      url          = {https://juser.fz-juelich.de/record/1025669},
}