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Bioinformatics Advance Access originally published online on November 28, 2007
Bioinformatics 2008 24(1):110-117; doi:10.1093/bioinformatics/btm486
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Monte Carlo feature selection for supervised classification

Michal Draminski 1, Alvaro Rada-Iglesias 2, Stefan Enroth 3, Claes Wadelius 2, Jacek Koronacki 1,{dagger} and Jan Komorowski 3,4,*,{dagger}

1Institute of Computer Science, Polish Academy of Science, Ordona 21, PL-01-237 Warsaw, Poland, 2Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 3The Linnaeus Centre for Bioinformatics, Uppsala University and The Swedish University for Agricultural Sciences, Box 758, SE-751 24 Uppsala, Sweden and 4Interdisciplinary Centre for Mathematical and Computer Modelling, Warsaw University, Poland

*To whom correspondence should be addressed.


  Abstract

Motivation: Pre-selection of informative features for supervised classification is a crucial, albeit delicate, task. It is desirable that feature selection provides the features that contribute most to the classification task per se and which should therefore be used by any classifier later used to produce classification rules. In this article, a conceptually simple but computer-intensive approach to this task is proposed. The reliability of the approach rests on multiple construction of a tree classifier for many training sets randomly chosen from the original sample set, where samples in each training set consist of only a fraction of all of the observed features.

Results: The resulting ranking of features may then be used to advantage for classification via a classifier of any type. The approach was validated using Golub et al. leukemia data and the Alizadeh et al. lymphoma data. Not surprisingly, we obtained a significantly different list of genes. Biological interpretation of the genes selected by our method showed that several of them are involved in precursors to different types of leukemia and lymphoma rather than being genes that are common to several forms of cancers, which is the case for the other methods.

Availability: Prototype available upon request.

Contact: jan.komorowski{at}lcb.uu.se

Associate Editor: Joaquin Dopazo

{dagger}The authors wish it to be known that, in their opinion, the last two authors should be regarded as joint First Authors.

Received on December 13, 2006; revised on August 28, 2007; accepted on September 25, 2007

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