STatistical Inference Relief (STIR) feature selection.

ABSTRACT:

Motivation

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 retain

SUBMITTER: Le TT 

PROVIDER: S-EPMC6477983 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature