Relevant feature set estimation with a knock-out strategy and random forests.

ABSTRACT: Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used approach is to analyze the data using univariate methods followed by post-hoc corrections that try to account for the data's multivariate nature. Although widely used, this approach may fail to recover from the adverse effects of the initial analysis when local effects are not strong. Multivariate pattern analysis (MVPA) is a powerful alternative to the univariate approach for identifying relevant variations. Jointly analyzing all the measures,

SUBMITTER: Ganz M 

PROVIDER: S-EPMC4618061 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature