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

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

Volume: 12 Issue: 5

Author(s): Loren Hansen, Ernestine A. Lee, Kevin Hestir, Lewis T. Williams and David Farrelly

Affiliation:

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

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.

Cite this article as:

Hansen Loren, Lee A. Ernestine, Hestir Kevin, Williams T. Lewis and Farrelly David, Controlling Feature Selection in Random Forests of Decision Trees Using a Genetic Algorithm: Classification of Class I MHC Peptides, Combinatorial Chemistry & High Throughput Screening 2009; 12 (5) . https://dx.doi.org/10.2174/138620709788488984