One-Class Support Vector Machine for Data Streams
| Autor: | Srinidhi Bhat, Sanjay Singh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Data stream mining Computer science 02 engineering and technology computer.software_genre Novelty detection Data modeling Support vector machine Kernel (linear algebra) 020901 industrial engineering & automation Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Information system 020201 artificial intelligence & image processing Data mining computer |
| Zdroj: | TENCON |
| DOI: | 10.1109/tencon50793.2020.9293814 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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