A particularly challenging class of PR problems in which the, generally required, representative set of data drawn from the second class is unavailable, has recently received much consideration under the guise of One-Class (OC) classification. In this paper, we extend the frontiers of OC classification by the introduction of a new field of problems open for analysis. In particular, we note that this new realm deviates from the standard set of OC problems based on the following characteristics: The data contains a temporal nature, the instances of the classes are "interwoven", and the labelling procedure is not merely impractical-it is almost, by definition, impossible, which results in a poorly defined training set. As a first attempt to tackle these problems, we present two specialized classification strategies denoted by Scenarios S1 and S2 respectively. In Scenarios S1, the data is such that standard binary and one-class classifiers can be applied. Alternatively, in Scenarios S2, the labelling challenge prevents the application of binary classifiers, and instead, dictates a novel application of OC classifiers. The validity of these scenarios has been demonstrated for the exemplary domain involving the Comprehensive Nuclear Test-Ban-Treaty (CTBT), for which our research endeavour has also developed a simulation model. As far as we know, our research in this field is of a pioneering sort, and the results presented here are novel.

Additional Metadata
Keywords Noisy Data, Novelty Detection, Pattern Recognition, Stochastically Episodic Events
Persistent URL dx.doi.org/10.1007/978-3-642-20039-7-44
Series Lecture Notes in Computer Science
Bellinger, C. (Colin), & Oommen, J. (2011). A new frontier in novelty detection: Pattern recognition of stochastically episodic events. In Lecture Notes in Computer Science. doi:10.1007/978-3-642-20039-7-44