PCA 4 DCA：主成分分析在树突状细胞算法中的应用
PCA 4 DCA: The Application Of Principal Component Analysis To The Dendritic Cell Algorithm
摘要：
树突状细胞算法（DCA）是人工免疫系统（AIS）领域的最新成员之一，它基于天然树突状细胞（DC）的行为模型。与其他AIS不同，DCA不依赖于训练数据，而是需要领域或专家知识来预先确定来自特定实例的输入信号与DCA所使用的三个类别之间的映射。这个数据预处理阶段已经收到了人工过度的批评？将数据发送到算法，这是不期望的。因此，在本文中，我们试图确定是否有可能使用主成分分析（PCA）技术自动分类输入数据，同时仍然产生有用的和准确的分类结果。集成系统进行了测试与生物特征数据集的压力识别的汽车驾驶员。实验结果表明，PCA应用于DCA的自动数据预处理是成功的。
英文摘要：
As one of the newest members in the field of artificial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-?tted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
