UM
Multilayer one-class extreme learning machine
Dai,Haozhen1,2; Cao,Jiuwen1,2,3; Wang,Tianlei1,2; Deng,Muqing1,2; Yang,Zhixin4
2019-07-01
Source PublicationNeural Networks
ISSN0893-6080
Volume115Pages:11-22
AbstractOne-class classification has been found attractive in many applications for its effectiveness in anomaly or outlier detection. Representative one-class classification algorithms include the one-class support vector machine (SVM), Naive Parzen density estimation, autoencoder (AE), etc. Recently, the one-class extreme learning machine (OC-ELM) has been developed for learning acceleration and performance enhancement. But existing one-class algorithms are generally less effective in complex and multi-class classifications. To alleviate the deficiency, a multilayer neural network based one-class classification with ELM (in short, as ML-OCELM) is developed in this paper. The stacked AEs are employed in ML-OCELM to exploit an effective feature representation for complex data. The effective kernel based learning framework is also investigated in the stacked AEs of ML-OCELM, leading to a multilayer kernel based OC-ELM (in short, as MK-OCELM). The MK-OCELM has advantages of less human-intervention parameters and good generalization performance. Experiments on 13 benchmark UCI classification datasets and a real application on urban acoustic classification (UAC) are carried out to show the superiority of the proposed ML-OCELM/MK-OCELM over the OC-ELM and several state-of-the-art algorithms.
KeywordKernel learning ML-OCELM OC-ELM One-class classification Outlier/anomaly detection
DOI10.1016/j.neunet.2019.03.004
URLView the original
Language英语
Scopus ID2-s2.0-85063345577
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Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorCao,Jiuwen
Affiliation1.School of Automation,Hangzhou Dianzi University,Zhejiang,310018,China
2.Artificial Intelligence Institute,Hangzhou Dianzi University,Zhejiang,310018,China
3.School of Electrical,Information and Media Engineering,University of Wuppertal,Wuppertal,42119,Germany
4.State Key Laboratory of Internet of Things for Smart City,Faculty of Science and Technology,University of Macau,Macau,China
Recommended Citation
GB/T 7714
Dai,Haozhen,Cao,Jiuwen,Wang,Tianlei,et al. Multilayer one-class extreme learning machine[J]. Neural Networks,2019,115:11-22.
APA Dai,Haozhen,Cao,Jiuwen,Wang,Tianlei,Deng,Muqing,&Yang,Zhixin.(2019).Multilayer one-class extreme learning machine.Neural Networks,115,11-22.
MLA Dai,Haozhen,et al."Multilayer one-class extreme learning machine".Neural Networks 115(2019):11-22.
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