Name
Abstract | ||
|---|---|---|
We propose a novel probabilistic semi-supervised anomaly detection framework for multi-dimensional systems with high correlation among variables. Our method is able to identify both abnormal instances and abnormal variables of an instance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-419-0-1109 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Anomaly detection,Data mining,Pattern recognition,Computer science,Correlation,Artificial intelligence,Probabilistic logic,Machine learning | Conference | 263 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Tong | 1 | 40 | 8.11 |
| Tetsuro Morimura | 2 | 5 | 1.83 |
| Einoshin Suzuki | 3 | 853 | 93.41 |
| Tsuyoshi Idé | 4 | 459 | 33.17 |