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中文摘要：
为了改善人工免疫识别系统的非线性能力，进一步优化分类器性能，提出了一种改进的人工免疫识别系统。新算法采用混合核函数来提升算法的非线性能力，同时，对记忆细个体进行适应度评估，淘汰低适应度的细胞来优化免疫分类器。改进的算法被应用于复杂UCI数据集的分类，分类结果与其他经典的分类算法的结果进行比较，结果显示该算法具有更好的分类性能。
英文摘要：
In order to improve the nonlinearity of Artificial Immune Recognition System（AIRS）and optimize the classifier, an improved AIRS algorithm is proposed. The new algorithm adopts hybrid kernel function to improve its nonlinearity, moreover, the individual memory cell is evaluated by its fitness score and the cells with low fitness scores are pruned to optimize the classifier. The improved algorithm has been applied to the complex UCI datasets classification, the results have been compared with the results achieved by other classic classifiers. The comparison shows that the algorithm achieves better classification performances.
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