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Consistent feature selection for pattern recognition in polynomial time
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
Linköping University, Department of Computer and Information Science, Database and information techniques. (ADIT)
Computional Medicine group KI.
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
2007 (English)In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 8, 589-612 p.Article in journal (Refereed) Published
Abstract [en]

We analyze two different feature selection problems: finding a minimal feature set optimal for classification (MINIMAL-OPTIMAL) vs. finding all features relevant to the target variable (ALL-RELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of data distributions for which there exist consistent, polynomial-time algorithms. We also prove that ALL-RELEVANT is much harder than MINIMAL-OPTIMAL and propose two consistent, polynomial-time algorithms. We argue that the distribution classes considered are reasonable in many practical cases, so that our results simplify feature selection in a wide range of machine learning tasks.

Place, publisher, year, edition, pages
2007. Vol. 8, 589-612 p.
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Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-38405Local ID: 44191OAI: oai:DiVA.org:liu-38405DiVA: diva2:259254
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13

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Nilsson, RolandPeña, Jose M.Tegnér, Jesper

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