Learning ensemble of decision trees through multifactorial genetic programming

Genetic programming (GP) has received considerable successes in machine learning tasks such as prediction and classification. Ensemble learning enables the collaboration of multiple classifiers and effectively improves the classification accuracy. Learning an ensemble of classifiers with GP can simply be achieved by repeated runs of GP; however, the computational cost will be multiplied as well. Recently, multifactorial evolution was proposed to concurrently solve multiple problems with a single population. This study utilizes the multifactorial evolution and designs a multifactorial genetic programming (MFGP) for efficiently learning an ensemble of decision trees. In the MFGP, each task is associated with one run of GP. The multifactorial evolution enables MFGP to evolve multiple GP classifiers for an ensemble in a single run, which saves a substantial amount of computational cost at repeated runs of GP. The experimental results show that MFGP can learn an ensemble with comparable accuracy, precision, and recall to conventional ensemble learning methods, whereas MFGP requires much less computational resource. The satisfactory outcomes validate the advantages of MFGP in ensemble learning.