Name
Abstract | ||
|---|---|---|
We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantages of adaptive isolation methods with the flexibility of preference embedding. Specifically, we propose to embed the data in a high dimensional space where an efficient tree-based method, PI-FOREST, is employed to compute an anomaly score. Experiments on synthetic and real datasets demonstrate that PIF favorably compares with state-of-the-art anomaly detection techniques, and confirm that PI-FOREST is better at measuring arbitrary distances and isolate points in the preference space. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPR48806.2021.9412658 | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
DocType | ISSN | Citations |
Conference | 1051-4651 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filippo Leveni | 1 | 0 | 0.34 |
| Magri, L. | 2 | 14 | 1.56 |
| Giacomo Boracchi | 3 | 324 | 30.49 |
| Cesare Alippi | 4 | 1040 | 115.84 |