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, a binary classifier (here v-SVM) is applied to separate the positive and the negative points. The results of this novel technique are compared with those of One-Class SVM and the Gaussian Mixture Model on several One-Class Classification tasks. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Bánhalmi, A., Kocsor, A., & Busa-Fekete, R. (2007). Counter-example generation-based one-class classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4701 LNAI, pp. 543–550). Springer Verlag. https://doi.org/10.1007/978-3-540-74958-5_51
Mendeley helps you to discover research relevant for your work.