]> 2026-04-04T05:34:26+02:00 Interactive Anomaly Detection 0 en Master Anomalies can be relevant or irrelevant to the end-user. The goal of this thesis is to propose a new interactive anomaly detection method to leverage the user-feedback and learn to suggest more relevant anomalies. Requires very good understanding of ML and data mining techniques (especially for anomaly detection). Good programming skills (preferably in Python) are also required. - Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 612-620. Society for Industrial and Applied Mathematics. https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69 - Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM. http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf - Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf - Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. IEEE Access. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963 Ongoing 4th of November 2019 to 29th May 2020 Interactive Anomaly Detection 2020-09-08T17:25:27Z 2459101.2260069 Interactive Anomaly Detection 0 [2,"smw-datavalue-property-restricted-annotation-use","Keywords"] Interactive Anomaly Detection# ERRd9c33d5ac8a89308063e13c4b212fa7c