%0 Conference Paper %1 decke2022ndnet %A Decke, Jens %A Schmeißing, Jörn %A Botache, Diego %A Bieshaar, Maarten %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2022 %I Springer %K imported itegpub isac-www NoveltyDetection Library MachineLearning UnsupervisedLearning AnomalyDetection %P 197--210 %R 10.1007/978-3-031-21867-5_13 %T NDNET: A Unified Framework for Anomaly and Novelty Detection %X 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.

@inproceedings{decke2022ndnet, abstract = {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.}, added-at = {2023-01-09T11:03:25.000+0100}, author = {Decke, Jens and Schmeißing, Jörn and Botache, Diego and Bieshaar, Maarten and Sick, Bernhard and Gruhl, Christian}, biburl = {https://www.bibsonomy.org/bibtex/28ffa06f3ecb841bfe80ef215899f7f7d/ies}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, doi = {10.1007/978-3-031-21867-5_13}, interhash = {b243ae5d3eac4bd7218b5ba82e7a19a0}, intrahash = {8ffa06f3ecb841bfe80ef215899f7f7d}, keywords = {imported itegpub isac-www NoveltyDetection Library MachineLearning UnsupervisedLearning AnomalyDetection}, pages = {197--210}, publisher = {Springer}, timestamp = {2023-01-09T11:03:25.000+0100}, title = {{NDNET}: {A} Unified Framework for Anomaly and Novelty Detection}, year = 2022 }