增加检测器覆盖非自体区域的简单方法

Simple Method of Increasing the Coverage of Nonself Region for Detector

免疫启发式否定选择算法应用于数据集中的异常检测。这种检测基于自体/非自体的识别,他的特征是具有通过自体或规则样本信息检测非自体样本的能力。所以,问题空间被分成两个不相交的子空间:一个子空间包含自体样本,另一个子空间包含促使通过否定选择算法生成检测器的样本。因此,否定选择算法的效率与检测器覆盖非自体空间范围成正比。在文章中,提出了一种扩大检测器覆盖范围的简单方法。

One of the intriguing applications of immune-inspired negative selection algorithm is anomaly detection in the datasets.Such a detection is based on the self/nonself discrimination and its characteristic feature is the ability of detecting nonself samples by using only information about the self,or regular,samples.Thus the problem space is splitted into two disjoint subspaces： One of them contains self samples and the second is covered by the samples which activate the detectors generated by the negative selection algorithms.Hence,the efficiency of negative selection algorithms is proportional to the degree of coverage of nonself subspace.In this paper,we present a simple method of increasing the coverage for detectors.