STatistical Inference Relief (STIR) feature selection

Bioinformatics
Trang T LeBrett A McKinney

Abstract

Relief is a family of machine learning algorithms that uses nearest-neighbors to select features whose association with an outcome may be due to epistasis or statistical interactions with other features in high-dimensional data. Relief-based estimators are non-parametric in the statistical sense that they do not have a parameterized model with an underlying probability distribution for the estimator, making it difficult to determine the statistical significance of Relief-based attribute estimates. Thus, a statistical inferential formalism is needed to avoid imposing arbitrary thresholds to select the most important features. We reconceptualize the Relief-based feature selection algorithm to create a new family of STatistical Inference Relief (STIR) estimators that retains the ability to identify interactions while incorporating sample variance of the nearest neighbor distances into the attribute importance estimation. This variance permits the calculation of statistical significance of features and adjustment for multiple testing of Relief-based scores. Specifically, we develop a pseudo t-test version of Relief-based algorithms for case-control data. We demonstrate the statistical power and control of type I error of the STIR f...Continue Reading

References

Oct 30, 2001·Behavioural Brain Research·Y BenjaminiI Golani
Feb 21, 2013·Molecular Systems Biology·Solip Park, Ben Lehner
Jul 22, 2018·Journal of Biomedical Informatics·Ryan J UrbanowiczJason H Moore
Jul 22, 2018·Journal of Biomedical Informatics·Ryan J UrbanowiczJason H Moore

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Datasets Mentioned

BETA
GM103456

Methods Mentioned

BETA
RNA-Seq

Software Mentioned

multiSURF
Evaporative Cooling ( pEC )
pEC
STIR
Relief
ReliefF
ReBATE
ReliefSeq

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