@inbook {IOPORT.06070314,
    author       = {de Souza, Erico N. and Matwin, Stan},
    title        = {Improvements to adaboost dynamic.},
    year         = {2012},
    booktitle    = {Advances in artificial intelligence. 25th Canadian conference on artificial intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28--30, 2012. Proceedings},
    isbn         = {978-3-642-30352-4},
    pages        = {293-298},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-30353-1_26},
    abstract     = {Summary: This paper presents recent results in extending the well known Machine Learning ensemble method, boosting. The main idea is to vary the ``weak'' base classifier with each step of the method, using a classifier which performs ``best'' on the data presented in that iteration. We show that the solution is sensitive to the loss function used, and that the exponential loss function is detrimental to the performance of this kind of boosting. An approach which uses a logistic loss function performs better, but tends to overfit with a growing number of iterations. We show that this drawback can be overcome with the use of resampling technique, taken from the research on learning from imbalanced data.},
    identifier   = {06070314},
}