Boosting by weighting boundary and erroneous samples.
Vanessa Gómez-Verdejo, Manuel Ortega-Moral, Jerónimo Arenas‐García, Aníbal Ramón Figueiras-Vidal · The European Symposium on Artificial Neural Networks · 2005
This paper shows that new and flexible criteria to resample populations in boosting algorithms can lead to performance improvements. Real Adaboost emphasis function can be divided into two different terms, the first only pays attention to the quadratic error of each pattern and the second takes only into account the proximity of each pattern to the boundary. Here, we incorporate an additional degree of freedom to this fixed emphasis function showing that a good tradeoff between these two components improves the performance of Real Adaboost algorithm. Re- sults over several benchmark problems show that an error rate reduction, a faster convergence and overfitting robustness can be achieved.