Abstract
In this paper, a novel method called online sequential extreme learning machine with under-sampling and over-sampling (OSELM-UO) for imbalanced Big data classification is proposed which combines the structures of under-sampling and over-sampling and applies online sequential extreme learning machine as its base model. The novel structure enables OSELM-UO performs well on both minority and majority classes and simultaneously overcomes the issues of information loss and overfitting. Moreover, when the dataset keeps growing, OSELM-UO can be applied without retraining all previous data. Experiments have been conducted for OSELM-UO and several imbalance learning methods over real-world datasets respectively under high imbalance ratio (IR) and large amount of samples and features. Through the analysis of the experimental results, OSELM-UO is shown to give the best results in various aspects.
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The work is financially supported by funding from University of Macau, project number MYRG2014-00083-FST, and from FDCT Macau, project number 050/2015/A.
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Du, J., Vong, CM., Chang, Y., Jiao, Y. (2018). Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification. In: Cao, J., Cambria, E., Lendasse, A., Miche, Y., Vong, C. (eds) Proceedings of ELM-2016. Proceedings in Adaptation, Learning and Optimization, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-57421-9_19
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DOI: https://doi.org/10.1007/978-3-319-57421-9_19
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