Boosting by weighting boundary and erroneous samples
نویسندگان
چکیده
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. Results over several benchmark problems show that an error rate reduction, a faster convergence and overfitting robustness can be achieved.
منابع مشابه
Boosting by weighting critical and erroneous samples
Real Adaboost is a well-known and good performance boosting method used to build machine ensembles for classification. Considering that its emphasis function can be decomposed in two factors that pay separated attention to sample errors and to their proximity to the classification border, a generalized emphasis function that combines both components by means of a selectable parameter, l, is pre...
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