V-detector: An efficient negative selection algorithm with "probably adequate" detector coverage
V-detector: An efficient negative selection algorithm with "probably adequate" detector coverage
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DOI：
10.1016/j.ins.2008.12.015
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发表时间：
2009-04
期刊：
影响因子：
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通讯作者：
Zhou Ji;D. Dasgupta
中科院分区：
文献类型：
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作者：
Zhou Ji;D. Dasgupta
本文提出了一种增强的否定选择算法（NSA），称为V-检测器。几个关键特征使这种方法在十年之久的NSA中取得了最先进的进展。首先，个体特定的大小（或匹配阈值）的检测器被用来最大限度地提高异常覆盖率在很少的额外成本。其次，在检测器生成算法中引入了统计估计，使得目标覆盖概率满足一定要求。在此基础上，基于数据点和匹配阈值的抽象概念，给出了该算法的一般形式。因此，它可以从目前的实值实现扩展到其他问题空间与不同的距离测量，数据/检测器表示方案等，通过使用一次过程来生成检测器集，该算法是更有效的比强进化的方法。它还包括将训练数据作为一个整体进行解释的选项，以便更清楚地检测自我和非自我区域之间的边界。讨论的重点是负选择算法的特点，而不是与其他策略的组合。
This paper describes an enhanced negative selection algorithm (NSA) called V-detector. Several key characteristics make this method a state-of-the-art advance in the decade-old NSA. First, individual-specific size (or matching threshold) of the detectors is utilized to maximize the anomaly coverage at little extra cost. Second, statistical estimation is integrated in the detector generation algorithm so the target coverage can be achieved with given probability. Furthermore, this algorithm is presented in a generic form based on the abstract concepts of data points and matching threshold. Hence it can be extended from the current real-valued implementation to other problem space with different distance measure, data/detector representation schemes, etc. By using one-shot process to generate the detector set, this algorithm is more efficient than strongly evolutionary approaches. It also includes the option to interpret the training data as a whole so the boundary between the self and nonself areas can be detected more distinctly. The discussion is focused on the features attributed to negative selection algorithms instead of combination with other strategies.