Is negative selection appropriate for anomaly detection?
Is negative selection appropriate for anomaly detection?
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DOI：
10.1145/1068009.1068061
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发表时间：
2005-06
期刊：
影响因子：
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通讯作者：
T. Stibor;Philipp H. Mohr;J. Timmis;C. Eckert
中科院分区：
文献类型：
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作者：
T. Stibor;Philipp H. Mohr;J. Timmis;C. Eckert
对Hamming和实值形状空间的负选择算法进行了综述。当应用于异常检测时，通过使用这些形状空间以及一般的否定选择算法来识别问题。提出了一种简单的自检测器分类原理，并与实值否定选择算法和单类支持向量机的分类性能进行了比较。早期的研究表明，真正的负面选择需要一个班级来学习。然而，本文的研究表明，当应用于异常检测时，实值否定选择和自检测器分类技术需要正反样本才能获得高的分类精度。然而，单类支持向量机只需要来自单个类的示例。
Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.