Evolutionary algorithms for constructing an ensemble of decision trees
This work addresses the need for more robust decision tree methods in machine learning, but it is incremental as it builds on existing evolutionary and ensemble techniques.
The authors tackled the problem of decision tree induction by proposing evolutionary algorithms for constructing ensembles, achieving better predictive performance than classical methods on several UCI datasets.
Most decision tree induction algorithms are based on a greedy top-down recursive partitioning strategy for tree growth. In this paper, we propose several methods for induction of decision trees and their ensembles based on evolutionary algorithms. The main difference of our approach is using real-valued vector representation of decision tree that allows to use a large number of different optimization algorithms, as well as optimize the whole tree or ensemble for avoiding local optima. Differential evolution and evolution strategies were chosen as optimization algorithms, as they have good results in reinforcement learning problems. We test the predictive performance of this methods using several public UCI data sets, and the proposed methods show better quality than classical methods.