Is negative selection appropriate for anomaly detection?

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 realvalue 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. Categories and Subject Descriptors I.5.2 [Pattern Recognition]: Design Methodology—Classifier design and evaluation, Pattern analysis ...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Thomas Stibor, Philipp H. Mohr, Jonathan Timmis, Claudia Eckert
• Keywords: GECCO 2005, Negative Selection, Negative Selection Algorithm, Real-valued Negative Selection