Real Adaboost Ensembles with Emphasized Subsampling
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Designing Model Based Classifiers by Emphasizing Soft Targets
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Designing Model Based Classifiers by Emphasizing Soft Targets
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Smoothed emphasis for boosting ensembles
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Progressively emphasizing samples that are difficult to classify correctly is the base for the recognized high performance of real Adaboost (RA) ensembles. The corresponding emphasis function can be written as a product of a factor that measures the quadratic error and a factor related to the proximity to the classification border; this fact opens the door to explore the potential advantages provided by using adjustable combined forms of these factors. In this paper, we introduce a principled procedure to select the combination parameter each time a new learner is added to the ensemble, just by maximizing the associated edge parameter, calling the resulting method the dynamically adapted weighted emphasis RA (DW-RA). A number of application examples illustrates the performance improvements obtained by DW-RA.