Parameter-free Streaming Distance-based Outlier Detection

被引：0

作者：

Giannoulidis, Apostolos ^{[1
]}

Nikolaidis, Nikodimos ^{[2
]}

Gounaris, Anastasios ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Thessaloniki, Greece

[2] Atlantis Engn, Thessaloniki, Greece

来源：

2024 IEEE 40TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOP, ICDEW | 2024年

关键词：

distance-based outlier detection; data streams; ALGORITHMS;

D O I：

10.1109/ICDEW61823.2024.00019

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dealing with anomaly detection in streaming data using distance-based techniques suffers from sensitivity to the input parameters. To solve this problem, we propose a parameter-free self-tuning solution, which can decide optimized parameters on the fly. Our technique achieves better or equal performance with traditional distance-based solutions even when these have been fine-tuned.

引用

页码：102 / 106

页数：5

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