Predicting minority class for suspended particulate matters level by extreme learning machine

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

Status	已發表Published
	Predicting minority class for suspended particulate matters level by extreme learning machine
	Vong, C. M.; Ip, W. F.; Wong, P.K.; Chiu, C. C.
	2014-03-01
Source Publication	Neurocomputing (SCI-E)
ISSN	0925-2312
Pages	136-144
Abstract	Suspended particulate matters (PM10) is considered as a harmful air pollutant. Many models attempt to predict numerical levels of PM10 but a simple, clearly defined classification of PM10 levels is more readily comprehensible to the general public rather than a numerical value. However, PM10 prediction model often suffers from data imbalance problem in the training dataset that results in failure to forecast the minority class of severe cases. In this study, a warning system using extreme learning machine (ELM), compared with support vector machine (SVM), was constructed to forecast the class of PM10 level: Good, Moderate, and Severe. An imbalance strategy called prior duplication was also applied to improve the forecast of minority class. The experimental comparisons between ELM and SVM demonstrate that ELM produces superior accuracy relative to SVM in forecasting minority class (Severe) of PM10 level with or without the imbalance strategy. Furthermore, our results show that the required training time and model size in the ELM model are much shorter and smaller than those of SVM respectively, leading to a more efficient and practical implementation of prediction model for large dataset. The performance superiority of ELM is also discussed in this paper.
Keyword	PM10 Extreme learning machine (ELM) Support vector machine (SVM) imbalance problem prior duplication
Language	英語English
The Source to Article	PB_Publication
PUB ID	9747
Document Type	Journal article
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Vong, C. M.
Recommended Citation GB/T 7714	Vong, C. M.,Ip, W. F.,Wong, P.K.,et al. Predicting minority class for suspended particulate matters level by extreme learning machine[J]. Neurocomputing (SCI-E), 2014, 136-144.
APA	Vong, C. M.., Ip, W. F.., Wong, P.K.., & Chiu, C. C. (2014). Predicting minority class for suspended particulate matters level by extreme learning machine. Neurocomputing (SCI-E), 136-144.
MLA	Vong, C. M.,et al."Predicting minority class for suspended particulate matters level by extreme learning machine".Neurocomputing (SCI-E) (2014):136-144.

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