Boosting-like Online Learning Ensemble (BOLE) [1] is a method based on Adaptable Diversity-based Online Boosting (ADOB) [2] and uses different strategies in order to increase its accuracy. Particularly, BOLE shows good performances in scenarios where concept drifts are frequent and/or abrupt.
[1] R. S. M. Barros, S. G. T. C. Santos, and P. M. Gonçalves Jr., "A Boosting-like Online Learning Ensemble" in 2016 International Joint Conference on Neural Networks (IJCNN), 2016, Vancouver. p. 1871.
[2] S. G. T. C. Santos, P. M. Gonçalves, Jr., G. D. S. Silva, and R. S. M. Barros, “Speeding up recovery from concept drifts” in Machine Learning and Knowledge Discovery in Databases, ser. LNCS. Springer, 2014, vol. 8726, pp. 179–194.
Silas Garrido - sgtcs@cin.ufpe.br