Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

Status	已發表Published
	Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification
	Du, J.; Vong, C. M.
	2017-05-26
Source Publication	Proceedings of ELM 2016
Pages	229-239
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.
Keyword	Big Data Imbalance Learning OS-ELM Under-sampling over-sampling
Language	英語English
The Source to Article	PB_Publication
PUB ID	38317
Document Type	Conference paper
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Vong, C. M.
Recommended Citation GB/T 7714	Du, J.,Vong, C. M.. Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification[C], 2017, 229-239.
APA	Du, J.., & Vong, C. M. (2017). Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification. Proceedings of ELM 2016, 229-239.

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