Title :
Cells clonal selection for Breast Cancer classification
Author :
Daoudi, Ryma ; Djemal, Khalifa ; Benyettou, Abdelkader
Author_Institution :
IBISC Lab., Univ. of Evry Val d`Essonne, Evry, France
Abstract :
In the last decade, several techniques of artificial intelligence proved their skills in the field of classification of cancer cells. We propose in this article a new idea of learning of the artificial immune systems (AIS) in the aim of improving CLONALG, one of the most popular algorithms in the field of the AIS. The principle of IMPROVED-CLONALG is to select the best cells to be cloned by calculating the averages of groups of the most competent cells in measures of similarity. The database used is Wisconsin Breast Cancer Database; promising results were found with compared to other implemented AIS algorithms.
Keywords :
artificial immune systems; cancer; cellular biophysics; learning (artificial intelligence); medical diagnostic computing; pattern classification; AIS algorithms; IMPROVED-CLONALG principle; Wisconsin Breast Cancer Database; artificial immune systems; artificial intelligence; breast cancer classification; cells clonal selection; learning; Breast cancer; Classification algorithms; Cloning; Databases; Educational institutions; Immune system; Breast Cancer; Classification; Cloning; Memory Cells; Mutate;
Conference_Titel :
Systems, Signals & Devices (SSD), 2013 10th International Multi-Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4673-6459-1
Electronic_ISBN :
978-1-4673-6458-4
DOI :
10.1109/SSD.2013.6564016
