Is cross-validation valid for small-sample microarray classification? | Zendy

Ulisses Braga-Neto | Zendy; Edward R. Dougherty | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Is cross-validation valid for small-sample microarray classification?

Author(s) -

Ulisses Braga-Neto,

Edward R. Dougherty

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btg419

Subject(s) - estimator , statistics , cross validation , replicate , computer science , outlier , variance (accounting) , mean squared error , sample size determination , variance inflation factor , pattern recognition (psychology) , artificial intelligence , mathematics , linear regression , accounting , business , multicollinearity

Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research