计算机工程 ›› 2019, Vol. 45 ›› Issue (9): 194-197,203.doi: 10.19678/j.issn.1000-3428.0052044

• 人工智能及识别技术 • 上一篇    下一篇

结合XGBoost的树突状细胞改进算法

杨晨, 梁意文, 谭成予, 周雯   

  1. 武汉大学 计算机学院, 武汉 430072
  • 收稿日期:2018-07-09 修回日期:2018-08-30 出版日期:2019-09-15 发布日期:2019-09-03
  • 作者简介:杨晨(1995-),女,硕士研究生,主研方向为计算机免疫;梁意文,教授、博士;谭成予,副教授、博士;周雯,博士研究生。
  • 基金项目:
    国家自然科学基金面上项目"计算机免疫智能的连续应答机制及其应用研究"(6187705)。

Optimized Dendritic Cell Algorithm Combined with XGBoost

YANG Chen, LIANG Yiwen, TAN Chengyu, ZHOU Wen   

  1. School of Computer Science, Wuhan University, Wuhan 430072, China
  • Received:2018-07-09 Revised:2018-08-30 Online:2019-09-15 Published:2019-09-03

摘要: 树突状细胞算法(DCA)要求输入3类信号,需要通过人工选取或统计学等方式提前进行特征提取。为准确、高效地提取特征,提出一种基于XGBoost的DCA。通过使用XGBoost算法迭代生成决策树,根据决策树的特征节点对数据集的特征指标进行提取与分类,并作为DCA的信号输入以实现算法优化。使用KDD99数据集进行实验,结果表明,与基于粗糙集的改进算法相比,该算法的准确率更高,最高可达0.859 00。

关键词: 树突状细胞算法, XGBoost算法, 决策树, 特征提取, 计算机免疫

Abstract: Dendritic Cell Algorithm(DCA) requires the input of 3 types of signals,which needs to extract features in advance through manual selection or statistics.To extract features accurately and efficiently,a DCA combined with XGBoost is proposed.The XGBoost algorithm is used to iteratively generate decision tree.According to the feature nodes of the decision tree,the characteristic indexes of the data set are extracted and classified,and used as the signal input of DCA to optimize the algorithm.Experiments are carried on KDD99 dataset,and the results show that the algorithm has higher accuracy than the improved algorithm based on rough set,which can reach up to 0.859 00.

Key words: Dendritic Cell Algorithm(DCA), XGBoost algorithm, decision tree, feature extraction, computer immune

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