A Regression Subset-Selection Strategy for Fat-Structure Data

Cristian Gatu (), Marko Sysi-Aho () and Matej Orešič ()
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Cristian Gatu: VTT Technical Research Centre of Finland
Marko Sysi-Aho: VTT Technical Research Centre of Finland
Matej Orešič: VTT Technical Research Centre of Finland

A chapter in COMPSTAT 2008, 2008, pp 349-358 from Springer

Abstract: Abstract A strategy is proposed for finding the most significant linear regression submodel for fat-structure data, that is when the number of variables n exceeds the number of available observations m. The method consists of two stages. First, a heuristic is employed to preselect a number of variables n S such that n S ≤m. The second stage performs an exhaustive search on the reduced list of variables. It employs a regression tree structure that generates all possible subset models. Non-optimal subtrees are pruned using a branch-and-bound device. Cross validation experiments on a real biomedical dataset are presented and analyzed.

Keywords: regression tree; branch-and-bound; model selection; fat-structure data (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_29

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DOI: 10.1007/978-3-7908-2084-3_29

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