TY - GEN
T1 - Online rare events detection
AU - Zhao, Jun Hua
AU - Li, Xue
AU - Dong, Zhao Yang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2007
Y1 - 2007
N2 - Rare events detection is regarded as an imbalanced classification problem, which attempts to detect the events with high impact but low probability. Rare events detection has many applications such as network intrusion detection and credit fraud detection. In this paper we propose a novel online algorithm for rare events detection. Different from traditional accuracy-oriented approaches, our approach employs a number of hypothesis tests to perform the cost/benefit analysis. Our approach can handle online data with unbounded data volume by setting up a proper moving-window size and a forgetting factor. A comprehensive theoretical proof of our algorithm is given. We also conduct the experiments that achieve significant improvements compared with the most relevant algorithms based on publicly available real-world datasets. © Springer-Verlag Berlin Heidelberg 2007.
AB - Rare events detection is regarded as an imbalanced classification problem, which attempts to detect the events with high impact but low probability. Rare events detection has many applications such as network intrusion detection and credit fraud detection. In this paper we propose a novel online algorithm for rare events detection. Different from traditional accuracy-oriented approaches, our approach employs a number of hypothesis tests to perform the cost/benefit analysis. Our approach can handle online data with unbounded data volume by setting up a proper moving-window size and a forgetting factor. A comprehensive theoretical proof of our algorithm is given. We also conduct the experiments that achieve significant improvements compared with the most relevant algorithms based on publicly available real-world datasets. © Springer-Verlag Berlin Heidelberg 2007.
UR - https://www.scopus.com/pages/publications/38049121658
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-38049121658&origin=recordpage
U2 - 10.1007/978-3-540-71701-0_126
DO - 10.1007/978-3-540-71701-0_126
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783540717003
VL - 4426 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1114
EP - 1121
BT - Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
PB - Springer Verlag
T2 - 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007)
Y2 - 22 May 2007 through 25 May 2007
ER -