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題名 Outlier detection in the concept drifting environment
作者 蔡瑞煌
Huang, Shin Ying
Lin, Jhe Wei
Tsaih, Rua-Huan
貢獻者 資管系
關鍵詞 Data handling; Decision support systems; Neural networks; Concept drifting; Decision supports; Moving window; Outlier Detection; Resistant learning; Statistics
日期 2016-10
上傳時間 1-九月-2017 10:07:26 (UTC+8)
摘要 Outliers are observations that lie far away from the fitting function deduced from the bulk of a set of observations. The outlier detection has become more challenging when the nature of data has involved with the `concept drifting.` To address this challenging issue, this study explores a decision support mechanism (DSM) for coping with the outlier detection problem in the concept drifting environment. The proposed DSM has the following features: (1) implementation of the resistant learning with envelope module via the adaptive single layer feed-forward neural network, (2) implementation of the incremental learning concept via the moving window technique, and (3) effectiveness and efficiency in terms of being more accurate in identifying outliers and of having to further investigate fewer outlier candidates. An experiment is implemented to validate the proposed DSM and the results are promising.
關聯 Proceedings of the International Joint Conference on Neural Networks, 2016-October, 31-37
資料類型 conference
DOI http://dx.doi.org/10.1109/IJCNN.2016.7727177
dc.contributor 資管系
dc.creator (作者) 蔡瑞煌zh_TW
dc.creator (作者) Huang, Shin Yingen_US
dc.creator (作者) Lin, Jhe Weien_US
dc.creator (作者) Tsaih, Rua-Huanen_US
dc.date (日期) 2016-10
dc.date.accessioned 1-九月-2017 10:07:26 (UTC+8)-
dc.date.available 1-九月-2017 10:07:26 (UTC+8)-
dc.date.issued (上傳時間) 1-九月-2017 10:07:26 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112491-
dc.description.abstract (摘要) Outliers are observations that lie far away from the fitting function deduced from the bulk of a set of observations. The outlier detection has become more challenging when the nature of data has involved with the `concept drifting.` To address this challenging issue, this study explores a decision support mechanism (DSM) for coping with the outlier detection problem in the concept drifting environment. The proposed DSM has the following features: (1) implementation of the resistant learning with envelope module via the adaptive single layer feed-forward neural network, (2) implementation of the incremental learning concept via the moving window technique, and (3) effectiveness and efficiency in terms of being more accurate in identifying outliers and of having to further investigate fewer outlier candidates. An experiment is implemented to validate the proposed DSM and the results are promising.
dc.format.extent 210 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings of the International Joint Conference on Neural Networks, 2016-October, 31-37en_US
dc.subject (關鍵詞) Data handling; Decision support systems; Neural networks; Concept drifting; Decision supports; Moving window; Outlier Detection; Resistant learning; Statistics
dc.title (題名) Outlier detection in the concept drifting environmenten_US
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1109/IJCNN.2016.7727177
dc.doi.uri (DOI) http://dx.doi.org/10.1109/IJCNN.2016.7727177