NDNET: A Unified Framework for Anomaly and Novelty Detection

被引：4

作者：

Decke, Jens ^{[1
]}

Schmeissing, Joern ^{[1
]}

Botache, Diego ^{[1
]}

Bieshaar, Maarten ^{[2
]}

Sick, Bernhard ^{[1
]}

Gruhl, Christian ^{[1
]}

机构：

[1] Univ Kassel, Dept Elect Engn & Comp Sci, Kassel, Germany

[2] Bosch Ctr Artificial Intelligence, Hildesheim, Germany

来源：

ARCHITECTURE OF COMPUTING SYSTEMS, ARCS 2022 | 2022年 / 13642卷

关键词：

Novelty detection; Library; Machine learning; Unsupervised learning; Anomaly detection;

D O I：

10.1007/978-3-031-21867-5_13

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.

引用

页码：197 / 210

页数：14

共 33 条

[1]

Ahmad A., 2021, APPL SCI, V11

[2] Data stream forecasting for system fault prediction [J].

Alzghoul, Ahmad ;

Lofstrand, Magnus ;

Backe, Bjorn .

COMPUTERS & INDUSTRIAL ENGINEERING, 2012, 62 (04) :972-978

[3]

[Anonymous], 2006, P INT C VER LARG DAT

[4]

BABCOCK B, 2002, P 21 ACM SIGMOD SIGA, P1, DOI DOI 10.1145/543613.543615

[5]

Bishop C., 2006, Pattern Recognition and Machine Learning

[6] Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands [J].

Botache, Diego ;

Bethke, Florian ;

Hardieck, Martin ;

Bieshaar, Maarten ;

Brabetz, Ludwig ;

Ayeb, Mohamed ;

Zipf, Peter ;

Sick, Bernhard .

2021 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS (ACSOS 2021), 2021, :120-130

[7] LOF: Identifying density-based local outliers [J].

Breunig, MM ;

Kriegel, HP ;

Ng, RT ;

Sander, J .

SIGMOD RECORD, 2000, 29 (02) :93-104

[8] The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation [J].

Chicco, Davide ;

Jurman, Giuseppe .

BMC GENOMICS, 2020, 21 (01)

[9] MINAS: multiclass learning algorithm for novelty detection in data streams [J].

de Faria, Elaine Ribeiro ;

de Leon Ferreira Carvalho, Andre Carlos Ponce ;

Gama, Joao .

DATA MINING AND KNOWLEDGE DISCOVERY, 2016, 30 (03) :640-680

[10]

Faria E.R., 2013, Proceedings of the 28th Annual ACM Symposium on Applied Computing, P795

← 1 2 3 4 →