@techreport{NoackSchmittSaretz2013, author = {Noack, Tino and Schmitt, Ingo and Saretz, Sascha}, title = {OVA-based multi-class classification for data stream anomaly detection}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-28187}, year = {2013}, abstract = {Mobile cyber-physical systems (MCPSs), such as the International Space Station, are equipped with sensors which produce sensor data streams. Continuous changes like wear and tear influence the system states of a MCPS continually during runtime. Hence, monitoring is necessary to provide reliability and to avoid critical damage. Although, the monitoring process is limited by resource restrictions. Therefore, the focal point of the present paper is on time-efficient multi-class data stream anomaly detection. Our contribution is bifid. First, we use a one-versusall classification model to combine a set of heterogeneous one-class classifiers consecutively. Such a chain of one-class classifiers provides a very flexible structure while the administrative overhead is reasonably low. Second, based on the classifier chain, we introduce classifier pre-selection.}, subject = {Informatik}, language = {en} }