Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

UM > Faculty of Science and Technology

Status	已發表Published
	Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine
	Vong, C. M.; Weng, F. I.; Chiu, C.C.; Wong, P. K.
	2015-06-01
Source Publication	Cognitive Computation (SCI-E)
ISSN	1866-9956
Pages	381-391
Abstract	Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level.
Keyword	Extreme Learning Machine Imbalanced learning Air Pollution
Language	英語English
The Source to Article	PB_Publication
PUB ID	15127
Document Type	Journal article
Collection	Faculty of Science and Technology
Recommended Citation GB/T 7714	Vong, C. M.,Weng, F. I.,Chiu, C.C.,et al. Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine[J]. Cognitive Computation (SCI-E), 2015, 381-391.
APA	Vong, C. M.., Weng, F. I.., Chiu, C.C.., & Wong, P. K. (2015). Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine. Cognitive Computation (SCI-E), 381-391.
MLA	Vong, C. M.,et al."Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine".Cognitive Computation (SCI-E) (2015):381-391.

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Affiliation No.
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh