In this paper we propose a comparative study of Artificial Neural Networks (ANN) and Artificial Immune Systems. Artificial Immune Systems (AIS) represent a novel paradigm in the field of computational intelligence based on the mechanisms that allow vertebrate immune systems to face attacks from foreign agents (called antigens). Several similarities as well as differences have been shown by Dasgupta in [1]. Here we present a comparative study of these two approaches considering evolutions of the concepts of ANN and AIS, respectively hybrid neural systems, Artificial Immune Recognition Systems (AIRS) and aiNet. We tried to establish a comparison among these three methods using a well known dataset, namely the Wisconsin Breast Cancer Database. We observed interesting trends in systems' performances and capabilities. Peculiarities of these systems have been analyzed, possible strength points and ideal contexts of application suggested. These and other considerations will be addressed in the rest of this manuscript.
Hybrid systems and artificial immune systems: performances and applications to biomedical research / Mastronardi, Giuseppe; Bevilacqua, Vitoantonio; DE MUSSO, C; Menolascina, F; Pedone, A. (LECTURE NOTES IN COMPUTER SCIENCE). - In: Advances in neural networks - ISNN 2007 : 4th international symposium on neural networks, Nanjing, China, June 3-7, 2007 / [a cura di] Liu, DR; Fei, SM; Hou, ZG; Zhang, HG; Sun, CY. - [s.l] : Springer, 2007. - ISBN 9783540723929. - pp. 1107-1114
Hybrid systems and artificial immune systems: performances and applications to biomedical research
MASTRONARDI, Giuseppe;BEVILACQUA, Vitoantonio;
2007-01-01
Abstract
In this paper we propose a comparative study of Artificial Neural Networks (ANN) and Artificial Immune Systems. Artificial Immune Systems (AIS) represent a novel paradigm in the field of computational intelligence based on the mechanisms that allow vertebrate immune systems to face attacks from foreign agents (called antigens). Several similarities as well as differences have been shown by Dasgupta in [1]. Here we present a comparative study of these two approaches considering evolutions of the concepts of ANN and AIS, respectively hybrid neural systems, Artificial Immune Recognition Systems (AIRS) and aiNet. We tried to establish a comparison among these three methods using a well known dataset, namely the Wisconsin Breast Cancer Database. We observed interesting trends in systems' performances and capabilities. Peculiarities of these systems have been analyzed, possible strength points and ideal contexts of application suggested. These and other considerations will be addressed in the rest of this manuscript.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
