摘要翻译：
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，基于天然树突状细胞的行为模型。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使用的三个类别之间的映射。这一数据预处理阶段受到了人工将数据过度分配给算法的批评，这是不可取的。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，同时仍然产生有用和准确的分类结果。利用生物特征数据集对集成系统进行了测试，用于汽车驾驶员压力识别。实验结果表明，将PCA应用于DCA中实现数据的自动预处理是成功的。
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英文标题：
《PCA 4 DCA: The Application Of Principal Component Analysis To The
  Dendritic Cell Algorithm》
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作者：
Feng Gu, Julie Greensmith, Robert Oates and Uwe Aickelin
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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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.
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PDF链接：
https://arxiv.org/pdf/1004.3460