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Evidential Artificial Immune Recognition System

  • Abir LahsoumiEmail author
  • Zied Elouedi
Conference paper
  • 835 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11775)

Abstract

Uncertainty is one of the main classification issues that must be handled carefully and not rejected in order to make better decisions. Artificial immune recognition system (AIRS) is an immune-inspired supervised learning classifier that has shown good and competitive classification results. It works perfectly in a certain context, however it is quite the opposite in an environment pervaded with uncertainty. To overcome this limitation, we propose a new approach combining the AIRS and belief function theory one of the well-know theories managing uncertainty. Experimentations on real data sets from the U.C.I machine learning repository show good performances of the proposed approach.

Keywords

Artificial immune recognition system (AIRS) Classification Uncertainty Belief function theory 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LARODEC, Institut Supérieur de Gestion de TunisUniversité de TunisLe BardoTunisia

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