Building boosted classification tree ensemble with genetic programming
Sašo Karakatič, Vili Podgorelec · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
Adaptive boosting (AdaBoost) is a method for building classification ensemble, which combines multiple classifiers built in an iterative process of reweighting instances. This method proves to be a very effective classification method, therefore it was the major part of our evolutionary inspired classification algorithm.