UNSUPERVISED CLASSIFICATION BASED NEGATIVE SELECTION ALGORITHM

Abstract

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.