结合XGBoost的树突状细胞改进算法

Optimized Dendritic Cell Algorithm Combined with XGBoost

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。

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.