SampleBoost: Improving boosting performance by destabilizing weak learners based on weighted error analysis

Learning from large, multi-class data sets poses great challenges to ensemble methods. The weak learner condition makes the conventional method inappropriate to handle multi-class classification, which leads to early termination of the training process. Also, elongated training time makes learning from large data set infeasible. To circumvent these issues, we present a novel method that integrates a sampling strategy and an error parameter that alters weighted error. Experiments were conducted with ten data sets from real-world applications. It is evident that our proposed method achieves greater performance and avoids early termination. In addition, our method significantly improves training efficiency and accommodates training with large data set.