Machine learning algorithm validation with a limited sample size

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

(2017) Gaël Varoquaux   NEUROIMAGE

Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines

(2017) Gaël Varoquaux et al.   NEUROIMAGE

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

(2017) Mohammad R. Arbabshirani et al.   NEUROIMAGE

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

(2016) Pegah Kassraian-Fard et al.   Frontiers in Psychiatry

Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy

(2015) Etienne Combrisson et al.   JOURNAL OF NEUROSCIENCE METHODS

Machine learning applications in genetics and genomics

(2015) Maxwell W. Libbrecht et al.   NATURE REVIEWS GENETICS

UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

(2015) Cathie Sudlow et al.   PLOS MEDICINE

A review of microarray datasets and applied feature selection methods

(2014) V. Bolón-Canedo et al.   INFORMATION SCIENCES

Cross-validation pitfalls when selecting and assessing regression and classification models

(2014) Damjan Krstajic et al.   Journal of Cheminformatics

Analysis of feature selection stability on high dimension and small sample data

(2013) David Dernoncourt et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Sample size planning for classification models

(2012) Claudia Beleites et al.   ANALYTICA CHIMICA ACTA

Predicting sample size required for classification performance

(2012) Rosa L Figueroa et al.   BMC Medical Informatics and Decision Making

Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation

(2008) Olivier Devos et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS