ML Anthology

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

Paramveer S. Dhillon, Brian Tomasik, Dean P. Foster, Lyle H. Ungar
ECML-PKDD 2009 pp. 276-289
doi:10.1007/978-3-642-04180-8_35 /ecmlpkdd/2009/dhillon2009ecmlpkdd-multitask/

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.

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Cite

Text

Dhillon et al. "Multi-Task Feature Selection Using the Multiple Inclusion Criterion (MIC)." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04180-8_35

Markdown

[Dhillon et al. "Multi-Task Feature Selection Using the Multiple Inclusion Criterion (MIC)." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/dhillon2009ecmlpkdd-multitask/) doi:10.1007/978-3-642-04180-8_35

BibTeX

@inproceedings{dhillon2009ecmlpkdd-multitask,
  title     = {{Multi-Task Feature Selection Using the Multiple Inclusion Criterion (MIC)}},
  author    = {Dhillon, Paramveer S. and Tomasik, Brian and Foster, Dean P. and Ungar, Lyle H.},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2009},
  pages     = {276-289},
  doi       = {10.1007/978-3-642-04180-8_35},
  url       = {https://mlanthology.org/ecmlpkdd/2009/dhillon2009ecmlpkdd-multitask/}
}