可调控误报率和漏报率的树突状细胞算法  

Dendritic cell algorithm with adjustable false positives and false negatives

作　　者：袁嵩[1,2] 

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430065 [2]智能信息处理与实时工业系统湖北省重点实验室,湖北武汉430065

出　　处：《计算机工程与科学》2014年第1期99-104,共6页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(60975031)

摘　　要：为了解决传统树突状细胞算法(DCA)对环境评判的盲目性,分析DCA权值矩阵对检测结果的影响,提出两种可调控误报率和漏报率的DCA。一种是改进的投票制DCA,即在树突状细胞(DC)状态转换准则中融入倾向因子,以求得对环境评判的公平,并通过对倾向因子的微调控制检测结果的误报率和漏报率;另一种是评分制DCA,即在DC状态转化阶段忽略对细胞环境的评判,改为直接对抗原进行评分,最后根据抗原的平均分分布调整异常阈值,以达到调控误报率和漏报率的目的。实验表明,两种算法均有效地实现了结果可控性,相比而言,评分制DCA可实现更为直观的调控。In order to overcome the blindness of evaluation of the context in the classical Dendritic Cell Algorithm （DCA）, how the weight matrix of DCA influences the detection results is analyzed, and two kinds of DCAs are proposed, which can adjust the false positives and false negatives. The first one is the improved voting DCA. The Tendency Factor （TF） is involved in the Dendritic Cell （DC） state transition to assess the context fairly, and through the fine adjustment of the TF the false positives and false negatives of the detection results are controlled. The other one is the scoring DCA. In DC state transition phase, the evaluation of the context is ignored, instead, the antigen is directly given a score, then according to the distribution of average scores of antigens the anomaly threshold value can be adjus- ted to control the false positives and false negatives. Experiments show that the two algorithms can both effectively realize controlling the results. Comparatively, the scoring DCA can realize more intuitive con- trol.

关 键 词：树突状细胞算法 异常检测 倾向因子 人工免疫 数据融合 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]