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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/112491

Title:	Outlier detection in the concept drifting environment
Authors:	蔡瑞煌 Huang, Shin Ying Lin, Jhe Wei Tsaih, Rua-Huan
Contributors:	資管系
Keywords:	Data handling;Decision support systems;Neural networks;Concept drifting;Decision supports;Moving window;Outlier Detection;Resistant learning;Statistics
Date:	2016-10
Issue Date:	2017-09-01 10:07:26 (UTC+8)
Abstract:	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.
Relation:	Proceedings of the International Joint Conference on Neural Networks, 2016-October, 31-37
Data Type:	conference
DOI link:	http://dx.doi.org/10.1109/IJCNN.2016.7727177
DOI:	10.1109/IJCNN.2016.7727177
Appears in Collections:	[Department of MIS] Proceedings

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