Cross-validation failure: small sample sizes lead to large error bars

23 June 2017

Papers citing "Cross-validation failure: small sample sizes lead to large error bars"

33 / 33 papers shown

Title
False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims Evangelia Christodoulou Annika Reinke Pascaline Andrè Patrick Godau P. Kalinowski ... Amber L. Simpson A. Kopp-Schneider Gaël Varoquaux O. Colliot Lena Maier-Hein 48 0 0 07 May 2025
A Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment Alwani Liyana Ahmad Jose Sanchez-Bornot R. Sotero Damien Coyle Zamzuri Idris Ibrahima Faye 29 3 0 29 May 2024
Statistical Agnostic Regression: a machine learning method to validate regression models Juan M Gorriz J. Ramírez F. Segovia Francisco J. Martínez-Murcia C. Jiménez-Mesa J. Suckling 33 0 0 23 Feb 2024
Is K-fold cross validation the best model selection method for Machine Learning? Juan M Gorriz F. Segovia J. Ramírez A. Ortiz J. Suckling 47 17 0 29 Jan 2024
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! Tirth Patel Fred Lu Edward Raff Charles K. Nicholas Cynthia Matuszek James Holt 40 3 0 25 Dec 2023
Introducing 3DCNN ResNets for ASD full-body kinematic assessment: a comparison with hand-crafted features Alberto Altozano M. E. Minissi Mariano Alcañiz Javier Marín-Morales 14 0 0 24 Nov 2023
Confidence Intervals for Performance Estimates in Brain MRI Segmentation Rosana El Jurdi Gaël Varoquaux O. Colliot 20 1 0 20 Jul 2023
Efficient Learning of Minimax Risk Classifiers in High Dimensions Kartheek Bondugula Santiago Mazuelas Aritz Pérez Martínez 19 0 0 11 Jun 2023
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity Shixiong Wang Haowei Wang Xinke Li Jean Honorio OOD 65 1 0 31 Jan 2023
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research Fabian Eitel Marc-Andre Schulz Moritz Seiler Henrik Walter K. Ritter AI4CE 24 42 0 20 Jan 2023
How precise are performance estimates for typical medical image segmentation tasks? Rosana El Jurdi O. Colliot UQCV 27 6 0 26 Oct 2022
Reproducibility in machine learning for medical imaging O. Colliot Elina Thibeau-Sutre Ninon Burgos OOD 35 8 0 12 Sep 2022
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity Nikhil V. S. Avula S. K. Veesam Sudarshan Behera S. Balasubramanian 31 8 0 23 Aug 2022
Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses Keith Harrigian Mark Dredze 30 3 0 22 Jun 2022
Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants Yu Luo Wenyu Chen Jiang Qiu Tao Jia 18 15 0 08 May 2022
Generalization Through The Lens Of Leave-One-Out Error Gregor Bachmann Thomas Hofmann Aurelien Lucchi 72 7 0 07 Mar 2022
A hypothesis-driven method based on machine learning for neuroimaging data analysis J. Górriz R. Martín-Clemente C. Puntonet A. Ortiz J. Ramírez J. Suckling 30 6 0 09 Feb 2022
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics Greg Kiar Yohan Chatelain A. Salari Alan C. Evans Tristan Glatard OOD 18 3 0 20 Sep 2021
Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability C. Jiménez-Mesa J. Ramírez J. Suckling Jonathan Voglein J. Levin Juan M Gorriz Alzheimer's Disease Neuroimaging Initiative Adni Dominantly Inherited Alzheimer Network (DIAN) 22 10 0 30 Mar 2021
Muddling Labels for Regularization, a novel approach to generalization Karim Lounici Katia Méziani Benjamin Riu OOD 8 1 0 17 Feb 2021
Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI P. Ghane Narges Zarnaghi Naghsh U. Braga-Neto 26 9 0 23 Dec 2020
A connection between the pattern classification problem and the General Linear Model for statistical inference Juan M Gorriz SIPBA group J. Suckling 19 7 0 16 Dec 2020
Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial Jussi Tohka M. Gils 17 89 0 31 Aug 2020
Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale L. Kuncheva Clare E. Matthews Álvar Arnaiz-González Juan José Rodríguez Diez 8 15 0 27 Aug 2020
Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs Fabian Eitel J. P. Albrecht M. Weygandt Friedemann Paul K. Ritter 26 2 0 23 Jul 2020
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning Kevin Maik Jablonka D. Ongari S. M. Moosavi B. Smit AI4CE 31 351 0 18 Jan 2020
Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities Juan M Gorriz et al. Mashrur Chowdhury 37 22 0 27 Dec 2019
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression Claas Flint Micah Cearns N. Opel R. Redlich David M. A. Mehler ... S. Clark B. Baune Xiaoyi Jiang U. Dannlowski Tim Hahn 19 87 0 13 Dec 2019
Biological sex classification with structural MRI data shows increased misclassification in transgender women Claas Flint K. Förster Sophie A. Koser C. Konrad P. Zwitserlood ... V. Arolt Tim Hahn Xiaoyi Jiang U. Dannlowski D. Grotegerd 11 17 0 24 Nov 2019
Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction Matthew C. Robinson R. Glen A. Lee 8 59 0 28 May 2019
Machine learning in resting-state fMRI analysis Meenakshi Khosla K. Jamison G. Ngo Amy Kuceyeski M. Sabuncu 24 168 0 30 Dec 2018
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning S. Raschka 83 765 0 13 Nov 2018
Extracting representations of cognition across neuroimaging studies improves brain decoding A. Mensch Julien Mairal B. Thirion Gaël Varoquaux AI4CE 36 15 0 17 Sep 2018