Pruning AdaBoost for Continuous Sensors Mining Applications

In this work, pruning techniques for the AdaBoost classifier are evaluated specially aimed for a continuous learning framework in sensors mining applications. To assess the methods, three pruning schemes are evaluated using standard machine-learning benchmark datasets, simulated drifting datasets and real cases. Early results obtained show that pruning methodologies approach and sometimes out-perform the no-pruned version of the classifier, being at the same time more easily adaptable to the drift in the training distribution. Future works are planned in order to evaluate the approach in terms of time efficiency and extension to big-data analysis.