Pruning AdaBoost for Continuous Sensors Mining Applications

Workshop on Ubiquitous Data Mining 2012

计算机科学

M. Rastgoo ; G. Lemaitre ; X. Rafael Palou ; F. Miralles ; P. Casale 1

Others  :  http://ceur-ws.org/Vol-960/poster2.pdf
PID  :  28060

学科分类：计算机科学（综合）

来源: CEUR

【 摘 要 】

In this work, pruning techniques for the AdaBoost clas-sifier are evaluated specially aimed for a continuous learning frame- work 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 dis- tribution. Future works are planned in order to evaluate the approach