计算机应用研究2011，Vol.28Issue(6)：2043-2045,3.DOI:10.3969/j.issn.1001-3695.2011.06.010
不同的距离测量方法对人工免疫识别系统的性能影响
Effect of different distance measure methods on performance of artificial immune recognition system
摘要
Abstract
In order to analyze the effect of different distance measure methods on the AIRS classification performance, this paper implemented AIRS with three different distance measure methods, i. e. , Euclidean distance, Manhattan distance and RBF based kernel space distance, used the classifiers to Iris, Hear and Wine problems. Compared the obtained three groups of resuits with each other with regard to the classification accuracy and memory cells. The results show that the AIRS implemented with Manhattan distance measure method reached the highest accuracies for Iris and Heart among the three versions of AIRS and the highest accuracy reached for Wine was the AIRS with kernel space distance measure method used. Moreover, the most compact memory population produced by the AIRS with kernel space distance measure method. It can be seen from the comparisons that different distance measure methods give effect on the performance of AIRS to some extent.关键词
人工免疫识别系统/距离测量方法/分类性能/UCI数据集Key words
artificial immune recognition system(AIRS)/ distance measure method/ classification performance/ UCI datasets分类
信息技术与安全科学引用本文复制引用
邓泽林,谭冠政,范必双,叶吉祥..不同的距离测量方法对人工免疫识别系统的性能影响[J].计算机应用研究,2011,28(6):2043-2045,3.基金项目
国家自然科学基金资助项目(60874070) （60874070）
湖南省科技计划资助项目(2010GK3067) （2010GK3067）
湖南省教育厅一般资助项目(10C0368) （10C0368）
