基于记忆细胞剪切和非线性资源分配的人工免疫识别系统

为减少人工免疫识别系统(AIRS)的记忆细胞数量并提高AIRS的分类准确率，提出一种基于记忆细胞剪切和非线性资源分配的人工免疫识别系统(PNAIRS).PNAIRS采用样本属性离散化来压缩训练空间，利用记忆细胞剪切来淘汰低适应度细胞，并使用非线性资源分配来优化分类器.PNAIRS对6个UCI数据集进行分类测试，测试结果与其它分类算法结果对比，显示PNAIRS具有较小规模的记忆细胞群体和较高的分类准确率，而且算法运行速度快.这表明PNAIRS算法是一个性能良好的分类算法，具有潜在的应用价值.

Memory Cell Pruning and Nonlinear Resource Allocation Based Artificial Immune Recognition System

To reduce the memory cells of artificial immune recognition system (AIRS) and improve AIRS classification performance,a memory cell pruning and nonlinear resource allocation based artificial immune recognition system (PNAIRS) is proposed. Attribute discretization pre-processing is adopted to compress the training space. Memory cell pruning operation is employed to eliminate the memory cells of low fitness scores,and nonlinear resource allocation is utilized to optimize the classifier. PNAIRS is applied to 6 UCI datasets classification,the classification performance is compared with other classifiers. PNAIRS generates small memory cell population and reaches high classification accuracy,and the classification is finished quickly. The results show that PNAIRS is a high-performance classifier,and it has potential application.