关键词: 人工免疫系统, 树突细胞算法, 自动数据预处理, 主成分分析法

Abstract: The Dendritic Cell algorithm（DCA） can efficiently and effectively process large datasets in terms of data size. However, data size is not the only concern when handling complex datasets, high dimensionality is often a bigger problem. Complexity occurs at the data preprocessing stage of the DCA when dimensionality reduction is required. Previously, the data pre-processing of the DCA is performed manually based on users’ expert knowledge of a given problem domain, which is time consuming and sometime difficult to achieve. In this paper, automating the data pre-processing for DCA is proposed using Principal Component Analysis（PCA）, which extracts and selects relevant features, and adapts the algorithm to characteristics of the underlying data. The application of PCA to the DCA in KDDCUP’99 data set shows feasibility and generates useful and accurate classification results.

Key words: Artificial Immune System（AIS）, Dendritic Cell Algorithm（DCA）, automatic data preprocessing, Principal Component Analysis（PCA）