Online rare events detection

Zhao, Jun Hua, Li, Xue and Dong, Zhao Yang (2007). Online rare events detection. In: Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, Nanjing, (1114-1121). May 22, 2007-May 25, 2007. doi:10.1007/978-3-540-71701-0_126


Author Zhao, Jun Hua
Li, Xue
Dong, Zhao Yang
Title of paper Online rare events detection
Conference name 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Conference location Nanjing
Conference dates May 22, 2007-May 25, 2007
Proceedings title Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication Year 2007
Sub-type Fully published paper
DOI 10.1007/978-3-540-71701-0_126
ISBN 9783540717003
ISSN 0302-9743
Volume 4426 LNAI
Start page 1114
End page 1121
Total pages 8
Abstract/Summary 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.
Subjects 1300 Biochemistry, Genetics and Molecular Biology
1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

 
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