Generative Adversarial Active Learning for Unsupervised Outlier Detection

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in h...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 32; H. 8; S. 1517 - 1528
Hauptverfasser:	Liu, Yezheng, Li, Zhe, Zhou, Chong, Jiang, Yuanchun, Sun, Jianshan, Wang, Meng, He, Xiangnan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Active learning Anomaly detection Computational modeling curse of dimensionality Data analysis Data models Datasets Gallium nitride generate potential outliers generative adversarial active learning Generative adversarial networks Generators Machine learning mode collapsing problem multiple-objective generative adversarial active learning Outlier detection Outliers (statistics) Training
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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