中文摘要：

为了高效地挖掘分类模型，提出了一种基于抗体克隆选择学说和免疫记忆理论的有监督分类算法MCIC。采用模糊C均值聚类产生的初始抗体和样本特征维的信息熵权重为算法提供了先验知识；在抗体种群进行全局搜索的基础上，设计了动态记忆单元局部搜索算子，用以加快抗体亲和力成熟速度；最终，根据最近邻原则实现了未知样本的类属划分，并采用美国加州大学标准数据集中的4个数据集分类和齿轮箱故障识别问题进行了仿真实验。研究结果表明，与CLOALG算法相比，该算法不仅具有更快的收敛速度，而且获得了更高分类准确率。

英文摘要：

To efficiently mining the classification model, a novel classification algorithm was put forward based on antibody clonal selection and immune memory principle. Initial antibody population from fuzzy C means clustering and feature weightings computed through the information entropy were employed to provide priori knowledge. To improving the hyper mutation rate, a dynamic memory unit evolvement operator was designed for fine-tune search while the antibody population carry out global search. Finally classification was performed in a nearest neighbor approach. Experimental results on four benchmark datasets from UCI data set repository and gear box failure data demonstrate that, compared with CLOALG artificial immune classifiers, the new classifier not only has faster convergence speed, but also can achieve higher classification accuracy.