GRASP forest: a new ensemble method for trees

Authors:
José F. Diez-Pastor;César García-Osorio;Juan J. Rodríguez;Andrés Bustillo
Affiliations:
University of Burgos;University of Burgos;University of Burgos;University of Burgos
Venue:
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Year:
2011

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Abstract

This paper proposes a method for constructing ensembles of decision trees: GRASP Forest. This method uses the metaheuristic GRASP, usually used in optimization problems, to increase the diversity of the ensemble. While Random Forest increases the diversity by randomly choosing a subset of attributes in each tree node, GRASP Forest takes into account all the attributes, the source of randomness in the method is given by the GRASP metaheuristic. Instead of choosing the best attribute from a randomly selected subset of attributes, as Random Forest does, the attribute is randomly chosen from a subset of selected good attributes candidates. Besides the selection of attributes, GRASP is used to select the split value for each numeric attribute. The method is compared to Bagging, Random Forest, Random Subspaces, AdaBoost and MutliBoost, being the results very competitive for the proposed method.