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Outlier detection refers to the identification of data points that deviate from a general data distribution.
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2019
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2013
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X. Yang, L. Tan, and L. He, “A robust least squares support vector machine for regression and classification with noise,” Neurocomputing , vol. 140, pp. 41–52, 2014
2014
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M. Pavlidou and G. Zioutas, Kernel density outlier detector . Springer, 2014
2014
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2015
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C. C. Aggarwal, “Outlier analysis,” in Data mining . Springer, 2015, pp. 237–263
2015
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2015
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S. Rayana, “Odds library,” 2016. [Online]. Available: http://odds.cs.stonybrook.edu
2016
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G. O. Campos, A. Zimek, J. Sander, R. J. G. B. Campello, B. Micenková, E. Schubert, I. Assent, and M. E. Houle, “On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study,” Data Mining Knowledge Discovery , vol. 30, no. 9-10, pp. 891–927, 2016
2016
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2019
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2019
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Y. Dou, Z. Liu, L. Sun, Y. Deng, H. Peng, and P. S. Yu, “Enhancing graph neural network-based fraud detectors against camouflaged fraudsters,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management , 2020, pp. 315–324
2020
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J. Zhao, X. Liu, Q. Yan, B. Li, M. Shao, and H. Peng, “Multi-attributed heterogeneous graph convolutional network for bot detection,” Information Sciences , vol. 537, pp. 380–393, 2020
2020
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Z. Li, Y. Zhao, and J. Fu, “SynC: A copula based framework for generating synthetic data from aggregated sources,” in IEEE International Conference on Data Mining Workshops (ICDMW) , 2020
2020
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2020
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R. Xu, Y. Guo, X. Han, X. Xia, H. Xiang, and J. Ma, “Opencda: an open cooperative driving automation framework integrated with co-simulation,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 1155–1162
2021
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2021
Later among the works it cites.
Y. Zhao, X. Hu, C. Cheng, C. Wang, C. Wan, W. Wang, J. Yang, H. Bai, Z. Li, C. Xiao, Y. Wang, Z. Qiao, J. Sun, and L. Akoglu, “SUOD: Accelerating large-scale unsupervised heterogeneous outlier detection,” Proceedings of Machine Learning and Systems , 2021
2021
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2021
Later among the works it cites.
Y. Zhao, R. Rossi, and L. Akoglu, “Automatic unsupervised outlier model selection,” in Advances in Neural Information Processing Systems , vol. 34, 2021
2021
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L. Akoglu, “Anomaly mining - past, present and future,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021 , Z. Zhou, Ed. ijcai.org, 2021, pp. 4932–4936. [Online]. Available: https://doi.org/10.24963/ijcai.2021/697
2021
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M. Naaman, “On the tight constant in the multivariate Dvoretzky-Kiefer-Wolfowitz inequality,” Statistics & Probability Letters , vol. 173, p. 109088, 2021
2021
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G. Pang, C. Shen, L. Cao, and A. V. D. Hengel, “Deep learning for anomaly detection: A review,” ACM Computing Surveys (CSUR) , vol. 54, no. 2, pp. 1–38, 2021
2021
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X. Hu, Y. Huang, B. Li, and T. Lu, “Uncovering the source of machine bias,” ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Machine Learning for Consumers and Markets Workshop , 2021
2021
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