An Adaptive Artificial Immune Network Classifier With Independent Suppression Threshold
Artificial immune algorithm (aiNet) is one of the algorithms of the artificial immune system that is introduced as clustering and filtering of redundant data. This algorithm is also used as a classifier. One of the most effective parameters in this network is the suppression threshold which is responsible for controlling the value of minimum distance between two antibodies in the training phase, and the recognition threshold between antibody and antigen in the testing phase. The efficiency of the results of aiNet algorithm depends on the suppression threshold parameter and due to the fact that the suppression threshold parameter depends on the input data, in this paper we introduce the vectorial suppression threshold parameter (vts) instead of suppression threshold in order to automatic tuning of this parameter. We present an adaptive system, based on the feedback system which is capable of adjusting separate value of the suppression threshold for each class. The proposed method is tested on UCI dataset and Corel image dataset. The results show that the proposed model is acceptably effective and advantageous in comparison with the base method and other classifier.
component Artificial Immune Network, Artificial Immune Classifier, Parameter Tuning, Adaptive system
Mohammad Rahnemaye Hedayat Amir Masud Eftekhari Moghadam
Department of Computer Engineering Islamic Azad University, Qazvin branch Qazvin, Iran
国际会议
杭州
英文
373-377
2011-08-26（万方平台首次上网日期，不代表论文的发表时间）