关键词: 人工免疫, 克隆选择, 分类, 马氏距离

Abstract: The traditional Clonal Selection Algorithm (CSA) can only provide supervised learning for a certain type of sample data, which may result in lower classification efficiency and accuracy, thus a multi-class supervised classification algorithms based on CSA was proposed. This algorithm can obtain optimal clustering center of the multi-class sample data at the same time. The appropriate value of antibodies consider both the same class similarities and the different class differences, thus the best cluster center is more representative. Classification experiments used UCI data and mangrove multispectral TM images, and obtained the overall classification accuracy of 92% and Kappa coefficient of 0.91. UCI data also got a good result. The results show that the algorithm is an effective classification algorithm.

Key words: artificial immune, clonal selection, classification, Mahalanobis distance