Abstract
Variable selection is a common problem in regression modelling with a myriad of applications. This paper proposes a new feature ranking algorithm (DEPTH) for variable selection in parametric regression based on permutation statistics and stability selection. DEPTH is: (i) applicable to any parametric regression task, (ii) designed to be run in a parallel environment, and (iii) adapts naturally to the correlation structure of the predictors. DEPTH was applied to a genome-wide association study of breast cancer and found evidence that there are variants in a pathway of candidate genes that are associated with a common subtype of breast cancer, a finding which would not have been discovered by conventional analyses.
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© 2013 Springer International Publishing Switzerland
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Makalic, E., Schmidt, D.F., Hopper, J.L. (2013). DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_9
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DOI: https://doi.org/10.1007/978-3-319-03680-9_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03679-3
Online ISBN: 978-3-319-03680-9
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