E-CLONALG: An Enhanced Classifier Developed from CLONALG

摘要

This paper proposes an improved version of CLONALG, Clone Selection Algorithm based on Artificial Immune System that matches with the conventional classifiers in terms of accuracy tested on the same data sets. Clonal Selection Algorithm is an artificial immune system model. Instead of randomly selecting antibodies, it is proposed to take k memory pools consisting of all the learning cases. Also, an array averaged over the pools is created and is considered for cloning. Instead of using the best clone and calculating the similarity measure and comparing with the original cell, here, k best clones were selected, the average similarity measure was evaluated and noise was filtered. This process enhances the accuracy from 76.9 to 94.2 %, ahead of the conventional classification methods.