UNSUPERVISED CLASSIFICATION BASED NEGATIVE SELECTION ALGORITHM - BENDIAB, ESMA; K. KHOLLADI, M. (Courrier du Savoir, 2014)
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.