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A Regression Subset-Selection Strategy for Fat-Structure Data

In: Compstat 2008

Author

Listed:
  • Cristian Gatu

    (VTT Technical Research Centre of Finland
    “Alexandru Ioan Cuza” University of Iasi, Faculty of Computer Science)

  • Marko Sysi-Aho

    (VTT Technical Research Centre of Finland)

  • Matej Orešič

    (VTT Technical Research Centre of Finland)

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.

Suggested Citation

  • Cristian Gatu & Marko Sysi-Aho & Matej Orešič, 2008. "A Regression Subset-Selection Strategy for Fat-Structure Data," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 349-358, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2084-3_29
    DOI: 10.1007/978-3-7908-2084-3_29
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