Combining SOM and local minimum enclosing spheres for novelty detection
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Robust novelty detection in the framework of a contamination neighbourhood
International Journal of Intelligent Information and Database Systems
Robust novelty detection in the framework of a contamination neighbourhood
International Journal of Intelligent Information and Database Systems
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Single-class minimax probability machines (MPMs) offer robust novelty detection with distribution-free worst case bounds on the probability that a pattern will fall inside the normal region. However, in practice, they are too cautious in labeling patterns as outlying and so have a high false negative rate (FNR). In this paper, we propose a more aggressive version of the single-class MPM that bounds the best case probability that a pattern will fall inside the normal region. These two MPMs can then be used together to delimit the solution space. By using the hyperplane lying in the middle of this pair of MPMs, a better compromise between false positives (FPs) and false negatives (FNs), and between recall and precision can be obtained. Experiments on the real-world data sets show encouraging results