Machine learning algorithm validation with a limited sample size

• Published in: PloS one Vol. 14; no. 11; p. e0224365
• Main Authors: Vabalas, Andrius, Gowen, Emma, Poliakoff, Ellen, Casson, Alexander J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.11.2019; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial intelligence; Autism; Bias; Bioinformatics; Biological markers; Biology and Life Sciences; Biomarkers; Biomedical Research - statistics & numerical data; Brain research; Classification; Computer and Information Sciences; Computer simulation; Data analysis; Data collection; Data Interpretation, Statistical; Data mining; Datasets; Diagnostic imaging; Estimates; Humans; Internet of Things; Learning algorithms; Machine Learning; Medical imaging; Medicine and Health Sciences; Methods; Neuroimaging; Neurology; Noise; Normal distribution; Parameters; Pattern recognition; Physical Sciences; Research and Analysis Methods; Sample Size; Social Sciences; Spectrum analysis; Studies; Technology; Test procedures; Tracking; United Kingdom > UK; England

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participant...