Scalable ensemble learning by adaptive sampling
Document Type
Conference Proceeding
Publication Date
12-1-2012
Abstract
Scalability has become an increasingly critical problem for successful data mining and knowledge discovery applications in real world where we often encounter extremely huge data sets that will render the traditional learning algorithms infeasible. Among various approaches to scalable learning, sampling techniques can be exploited to address the issue of scalability. This paper presents a brief outline on how to utilize the new sampling method in [3] to develop a scalable ensemble learning method with Boosting. Preliminary experimental results using benchmark data sets from the UC-Irvine ML data repository are also presented confirming the efficiency and competitive prediction accuracy of the proposed adaptive boosting method. © 2012 IEEE.
Publication Source (Journal or Book title)
Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
First Page
622
Last Page
625
Recommended Citation
Chen, J. (2012). Scalable ensemble learning by adaptive sampling. Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, 1, 622-625. https://doi.org/10.1109/ICMLA.2012.115