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题名:	Outlier detection in the concept drifting environment
作者:	蔡瑞煌 Huang, Shin Ying Lin, Jhe Wei Tsaih, Rua-Huan
贡献者:	資管系
关键词:	Data handling;Decision support systems;Neural networks;Concept drifting;Decision supports;Moving window;Outlier Detection;Resistant learning;Statistics
日期:	2016-10
上传时间:	2017-09-01 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
数据类型:	conference
DOI 連結:	http://dx.doi.org/10.1109/IJCNN.2016.7727177
DOI:	10.1109/IJCNN.2016.7727177
显示于类别:	[資訊管理學系] 會議論文

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