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Controlling Feature Selection in Random Forests of Decision Trees Using a Genetic Algorithm: Classification of Class I MHC Peptides

[ Vol. 12 , Issue. 5 ]

Author(s):

Loren Hansen, Ernestine A. Lee, Kevin Hestir, Lewis T. Williams and David Farrelly   Pages 514 - 519 ( 6 )

Abstract:


Feature selection is an important challenge in many classification problems, especially if the number of features greatly exceeds the number of examples available. We have developed a procedure - GenForest - which controls feature selection in random forests of decision trees by using a genetic algorithm. This approach was tested through our entry into the Comparative Evaluation of Prediction Algorithms 2006 (CoEPrA) competition (accessible online at: http://www.coepra.org). CoEPrA was a modeling competition organized to provide an objective testing for various classification and regression algorithms via the process of blind prediction. In the competition GenForest ranked 10/23, 5/16 and 9/16 on CoEPrA classification problems 1, 3 and 4, respectively, which involved the classification of type I MHC nonapeptides i.e. peptides containing nine amino acids. These problems each involved the classification of different sets of nonapeptides. Associated with each amino acid was a set of 643 features for a total of 5787 features per peptide. The method, its application to the CoEPrA datasets, and its performance in the competition are described.

Keywords:

Decision trees, random forests, feature selection, genetic algorithms, evolutionary computation

Affiliation:

National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA.



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