One-class support vector machine for data streams

Srinidhi Bhat, Sanjay Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1130-1135
Number of pages6
ISBN (Electronic)9781728184555
DOIs
Publication statusPublished - 16-11-2020
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: 16-11-202019-11-2020

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2020-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
CountryJapan
CityVirtual, Osaka
Period16-11-2019-11-20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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