One-class Classification Methods via Automatic Counter-example Generation
نویسنده
چکیده
Here we propose novel methods for the One-Class Classification task and examine their applicability. Essentially, these methods extend the training set – which contains only positive examples – with artificially generated counterexamples. After, a two-class classifier is used to separate them. In this paper following a description of the existing and the newly proposed methods some problematic issues are investigated theoretically and studied empirically by applying these methods to artificial datasets. Then their efficiency is compared to those of other one-class classification methods.
منابع مشابه
Counter-Example Generation-Based One-Class Classification
For One-Class Classification problems several methods have been proposed in the literature. These methods all have the common feature that the decision boundary is learnt by just using a set of the positive examples. Here we propose a method that extends the training set with a counter-example set, which is generated directly using the set of positive examples. Using the extended training set, ...
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