Clonal Selection-Based Neural Classifier

Artificial Immune Systems (AIS) constitute an emerging and promising field, and have been applied to pattern recognition and classification tasks to a limited extent so far. This work is a first attempt of applying the clonal selection principle to the training of MultiLayer Perceptrons (MLPs). The Clonal Selectionbased Neural Classifier (CSNC) uses the basic concepts of clonal selection to evolve MLPs, which are represented as real-valued linear antibodies. The proposed system is actually a multi-classifier, consisting of multiple sets of MLPs, each one devoted to the recognition of a different class of the input data. The final trained classifier is comprised of the best MLPs from each set. The proposed classifier is tested against a set of benchmark problems and yields promising results.

• Type: Conference
• Year: 2008
• Where: HIS
• Authors: Aris Lanaridis, Vasileios Karakasis, Andreas Stafylopatis
• Keywords: Clonal Selection, Clonal Selection Principle, HIS 2008, Information Technology, Selectionbased Neural Classifier