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Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)

  • Paramveer S. Dhillon
  • Brian Tomasik
  • Dean Foster
  • Lyle Ungar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)

Abstract

We address the problem of joint feature selection in multiple related classification or regression tasks. When doing feature selection with multiple tasks, usually one can “borrow strength” across these tasks to get a more sensitive criterion for deciding which features to select. We propose a novel method, the Multiple Inclusion Criterion (MIC), which modifies stepwise feature selection to more easily select features that are helpful across multiple tasks. Our approach allows each feature to be added to none, some, or all of the tasks. MIC is most beneficial for selecting a small set of predictive features from a large pool of potential features, as is common in genomic and biological datasets. Experimental results on such datasets show that MIC usually outperforms other competing multi-task learning methods not only in terms of accuracy but also by building simpler and more interpretable models.

Keywords

Feature Selection Code Scheme Minimum Description Length Breast Cancer Dataset Multitask Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paramveer S. Dhillon
    • 1
  • Brian Tomasik
    • 2
  • Dean Foster
    • 3
  • Lyle Ungar
    • 1
  1. 1.CIS DepartmentUniversity of PennsylvaniaPhiladelphiaU.S.A.
  2. 2.Computer Science DepartmentSwarthmore CollegeU.S.A.
  3. 3.Statistics DepartmentUniversity of PennsylvaniaPhiladelphiaU.S.A.

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