Clustering Model of False Positive Elimination in Moroccan Fiscal Fraud Detection
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-36671-1_12
Reference14 articles.
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2. Gall, T., Maniadis, Z.: Evaluating solutions to the problem of false positives. Res. Policy 48(2), 506–515 (2019). https://doi.org/10.1016/j.respol.2017.12.005
3. Hu, L., Li, T., Xie, N., Hu, J.: False positive elimination in intrusion detection based on clustering. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015, pp. 519–523 (2016). https://doi.org/10.1109/FSKD.2015.7381996
4. Kotu, V., Deshpande, B.: Anomaly detection. Data Sci. 447–6510 (2019). https://doi.org/10.1016/B978-0-12-814761-0.00013-7
5. Leite, R.A., Gschwandtner, T., Miksch, S., Gstrein, E., Kuntner, J.: Visual analytics for event detection: focusing on fraud. Vis. Inf. (2018). https://doi.org/10.1016/j.visinf.2018.11.001
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