Rule-based OneClass-DS learning algorithm
•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...
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| Published in | Applied soft computing Vol. 35; pp. 267 - 279 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.10.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1568-4946 1872-9681 |
| DOI | 10.1016/j.asoc.2015.05.043 |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Cios, Krzysztof J. Nguyen, Dat T. |
| Author_xml | – sequence: 1 givenname: Dat T. surname: Nguyen fullname: Nguyen, Dat T. email: dat.nguyentien1780@hoasen.edu.vn organization: Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, VA, USA – sequence: 2 givenname: Krzysztof J. surname: Cios fullname: Cios, Krzysztof J. email: kcios@vcu.edu organization: Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, VA, USA |
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| Keywords | Outlier detection One-class learning algorithm: OneClass-DS Anomaly detection Novelty detection |
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| SubjectTerms | Anomaly detection Novelty detection One-class learning algorithm: OneClass-DS Outlier detection |
| Title | Rule-based OneClass-DS learning algorithm |
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