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Rule-based OneClass-DS learning algorithm
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DOI：10.1016/j.asoc.2015.05.043.png)
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Abstract
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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. Published by Elsevier B.V.
Keywords:
One class learning algorithm: OneClass-DS
Outlier detection
Anomaly detection
Novelty detection
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