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dc.contributor.authorNguyen, Tien Dat
dc.contributor.authorCios, Krzysztof J.
dc.date.issued2015
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/11026
dc.description.abstractAbstract One-class learning algorithms are used in situations when training data are available only for one class, called target class. Data for other class(es), called outliers, are not available. One-class learning algorithms are used for detecting outliers, or novelty, in the data. The common approach in one-class learning is to use density estimation techniques or adapt standard classification algorithms to define a decision boundary that encompasses only the target data. In this paper, we introduce OneClass-DS learning algorithm that combines rule-based classification with greedy search algorithm based on density of features. Its performance is tested on 25 data sets and compared with eight other one-class algorithms; the results show that it performs on par with those algorithms.
dc.formatPp. 267-279
dc.language.isoen
dc.sourceApplied Soft Computing. Volume 35
dc.subjectOne-class learning algorithm
dc.subjectOutlier detection
dc.subjectAnomaly detection
dc.subjectNovelty detection
dc.titleRule-based OneClass-DS learning algorithm
dc.typeArticle


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