【摘要】 树突状细胞算法(DCA)是一种模拟人体免疫系统中抗原提呈的算法,可以快速有效地将输入数据分为正常和异常,然而现有DCA模型普遍存在形式化描述不清晰且信号提取受人工经验影响的不足。为此,在hDCA模型的基础上,提出一种基于数字微分的函数化DCA模型。在预处理阶段引入数字微分方法,根据数据变化趋势自适应提取信号并随机动态采样抗原,去除对时序敏感的数据序列。在此基础上,对输入信号加以融合得到决策信号,并进行抗原背景环境分类处理。将ndhDCA、DCA和hDCA应用于WBC和KDD99数据集进行对比,实验结果表明,ndhDCA对有序数据集和无序数据集均具有高准确率和低误报率,同时可降低输入数据顺序的敏感性。更多还原

【Abstract】 The Dendritic Cell Algorithm(DCA) is an algorithm for simulating antigen presentation in the human immune system,which can divide input data into normal and abnormal data quickly and effectively.However,the existing DCA models are generally lack of clear formal description and their signal extraction is affected by artificial experience.To address the problems,this paper proposes a numerical differentiation-based functional dendritic cell model,named ndhDCA,by improving the hDCA model.In the preprocessing stage,the numerical differentiation method is introduced to extract the signal adaptively according to the trend of data change and to randomly and dynamically sample the antigen to remove the time-sensitive data sequence.On this basis,the input signal is fused to obtain the decision signal,and the antigen background environment is classified.ndhDCA,DCA and hDCA are compared on WBC and KDD99 data sets.The experimental results show that ndhDCA has higher accuracy and lower false positive rate in both ordered data sets and unordered data sets,and overcomes the sensitivity of the data sequence.更多还原