Balanced Sampling Method for Imbalanced Big Data Using AdaBoost

Hong Gu, Tao Song


With the arrival of the era of big data, processing large volumes of data at much faster rates has become more urgent and attracted more and more attentions. Furthermore, many real-world data applications present severe class distribution skews and the underrepresented classes are usually of concern to researchers. Variants of boosting algorithm have been developed to cope with the class imbalance problem. However, due to the inherent sequential nature of boosting, these methods can not be directly applied to efficiently handle largescale data. In this paper, we propose a new parallelized version of boosting, AdaBoost.Balance, to deal with the imbalanced big data. It adopts a new balanced sampling method which combines undersampling methods with oversampling methods and can be simultaneously calculated by multiple computing nodes to construct a final ensemble classifier. Consequently, it is easily implemented by the parallel processing platform of big data such as the MapReduce framework.


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Paper Citation

in Harvard Style

Gu H. and Song T. (2015). Balanced Sampling Method for Imbalanced Big Data Using AdaBoost . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015) ISBN 978-989-758-070-3, pages 189-194. DOI: 10.5220/0005254601890194

in Bibtex Style

author={Hong Gu and Tao Song},
title={Balanced Sampling Method for Imbalanced Big Data Using AdaBoost},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)},

in EndNote Style

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)
TI - Balanced Sampling Method for Imbalanced Big Data Using AdaBoost
SN - 978-989-758-070-3
AU - Gu H.
AU - Song T.
PY - 2015
SP - 189
EP - 194
DO - 10.5220/0005254601890194