UNSUPERVISED CLASSIFICATION BASED NEGATIVE SELECTION ALGORITHM

  • ESMA BENDIAB Modeling and Implementation of Complex Systems Laboratory(MISC Laboratory)
  • M. K. KHOLLADI Modeling and Implementation of Complex Systems Laboratory (MISC Laboratory)

Résumé

In  the  last decade, artificial  life has been considered as a promising area  for  rising challenges  to unresolved computational
problems. Inspired by natural phenomena, its study focuses on the exploration of complex systems. Neuronal networks, genetic
algorithms  and more  recently  artificial  immune  systems  are  examples. Artificial  Immune  Systems  (AIS)  are  one  type  of
intelligent algorithms  inspired by  the principles and processes of  the human  immune system. Emulating  the discrimination
mechanism of the natural system, negative selection algorithm of AIS has been successfully applied on change and anomaly
detection.  
This paper describes  initial  investigations  in applying negative  selection algorithm on pixel classification by maintaining a
population of detectors that remove undesired patterns. Its purpose is to find several detectors which do not match to self in the
population. We make use of an Euclidian space with an Euclidian performance measure on color images. The experimental
show promising results. The obtained classifier is effective and feasible.

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Comment citer
BENDIAB, ESMA; K. KHOLLADI, M.. UNSUPERVISED CLASSIFICATION BASED NEGATIVE SELECTION ALGORITHM. Courrier du Savoir, [S.l.], v. 14, mai 2014. ISSN 1112-3338. Disponible à l'adresse : >http://revues.univ-biskra.dz/index.php/cds/article/view/408>. Date de consultation : 15 jui. 2020
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