UM  > 科技學院  > 電腦及資訊科學系
Predicting minority class for suspended particulate matters level by extreme learning machine
Vong C.-M.1; Ip W.-F.2; Wong P.-K.3; Chiu C.-C.1
2014
Source PublicationNeurocomputing
ISSN9252312
Volume128Pages:136
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, the 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. © 2013 Elsevier B.V.

KeywordExtreme Learning Machine (Elm) Imbalance Problem Pm10 Prior Duplication Support Vector Machine (Svm)
DOI10.1016/j.neucom.2012.11.056
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000331851700017
The Source to ArticleScopus
全文获取链接
引用统计
被引频次[WOS]:29   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorVong C.-M.
Affiliation1.Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China
2.Univ Macau, Fac Sci & Technol, Macau, Peoples R China
3.Univ Macau, Dept Electromech Engn, Macau, Peoples R China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
推荐引用方式
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,2014,128:136.
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,128,136.
MLA Vong C.-M.,et al."Predicting minority class for suspended particulate matters level by extreme learning machine".Neurocomputing 128(2014):136.
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
Google Scholar
中相似的文章 Google Scholar
[Vong C.-M.]的文章
[Ip W.-F.]的文章
[Wong P.-K.]的文章
Baidu academic
中相似的文章 Baidu academic
[Vong C.-M.]的文章
[Ip W.-F.]的文章
[Wong P.-K.]的文章
Bing Scholar
中相似的文章 Bing Scholar
[Vong C.-M.]的文章
[Ip W.-F.]的文章
[Wong P.-K.]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。