关键词: 人工免疫系统, 不完备数据, 分类

Abstract: Artificial Immune Recognition System（AIRS） that is inspired by natural immune system has been developed as an efficient classifier. But the number of memory cells is too large and the accuracy of AIRS is not high, especially in the case of incomplete data. To solve the problem, this paper presents Immune Classification Algorithm Under（ICAU） incomplete data. It introduces semi-supervised learning, classifier fusion and vote to decision ideas, uses multiple AIRS classifiers to learn to refine each other. In the UCI data sets, experimental results prove the validity of the ICAU algorithm.

Key words: artificial immune, incomplete data, classification