Comments on real-valued negative selection vs. real-valued positive selection and one-class SVM

Thomas Stibor*, Jonathan Timmis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

14 Citations (SciVal)

Abstract

Real-valued negative selection (RVNS) is an immune-inspired technique for anomaly detection problems. It has been claimed that this technique is a competitive approach, comparable to statistical anomaly detection approaches such as one-class Support Vector Machine. Moreover, it has been claimed that the complementary approach to RVNS, termed real-valued positive selection, is not a realistic solution. We investigate these claims and show that these claims can not be sufficiently supported.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
PublisherIEEE Press
Pages3727-3734
Number of pages8
ISBN (Print)1424413400, 9781424413409
DOIs
Publication statusPublished - 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sept 200728 Sept 2007

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country/TerritorySingapore
Period25 Sept 200728 Sept 2007

Keywords

  • support vector machines
  • detectors
  • phase detection
  • immune system
  • support vector machine classification
  • pattern classification
  • proteins
  • automatic testing
  • machine learning
  • computer science

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