Controlling feature selection in random forests of decision trees using a genetic algorithm: classification of class I MHC peptides.
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.
Hansen
Loren
L
Program in Bioinformatics, Boston University, Boston, MA 02215, USA. hansenlo@ncbi.nlm.nih.gov
Lee
Ernestine A
EA
Hestir
Kevin
K
Williams
Lewis T
LT
Farrelly
David
D
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry / Q000276:immunology; D003663:Decision Trees; D005805:Genes, MHC Class I; D015395:Histocompatibility Antigens Class I / Q000235:genetics*; D006801:Humans; D010455:Peptides / Q000737:chemistry*
D000596:Amino Acids; D015395:Histocompatibility Antigens Class I; D010455:Peptides