Cross-Entropy Clustering Approach to One-Class Classification

Spurek, Przemysław; Wójcik, Mateusz; Tabor, Jacek

doi:10.1007/978-3-319-19324-3_43

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One-class Classification1

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2018

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“…If the similarity level is below a given threshold, the new instance is labeled as outlier. [46]. -Boundary-based methods concentrate on estimating only the enclosing boundary for the target class, assuming that such a boundary will be a sufficient descriptor [3,48].…”

Section: One-class Classificationmentioning

confidence: 99%

Instance reduction for one-class classification

et al. 2018

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Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multi-class problems providing very competitive results. However, this issue was rarely addressed in the context of one-class classification. In

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Section: One-class Classificationmentioning

confidence: 99%