One-Class Support Vector Machine for Data Streams
Srinidhi Bhat, Sanjay Kumar Singh · 2020
In various information systems, application learning algorithms have to act in a dynamic environment where the acquired data is in data streams. In contrast to static data mining, processing streams introduce an array of computational and algorithmic stipulations. With the continuous input of data in data streams, one would like a mechanism that automatically identifies unusual events in the time series. The topic has been in the limelight as it has huge potential for real-time activities. To show the algorithm's robustness, we have trained the classifier to multiple activities and its success in identifying each activity. The paper explores the possibility of using the One-Class Support Vector Machine (OCSVM) for novelty detection in data streams.