Rule-based OneClass-DS learning algorithm

• Published in: Applied soft computing Vol. 35; pp. 267 - 279
• Main Authors: Nguyen, Dat T., Cios, Krzysztof J.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2015
• Subjects: Anomaly detection; Novelty detection; One-class learning algorithm: OneClass-DS; Outlier detection; Outlier detection; One-class learning algorithm: OneClass-DS; Anomaly detection; Novelty detection
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.05.043

•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 approaching one-class learning is to use density estimation techniques or adapt standard classification algorithms to define a decision boundary that encompasses 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 one-class algorithms; the results show that it performs on par with the eight algorithms. 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.