Rule-based OneClass-DS learning algorithm
| dc.contributor.author | Nguyen, Tien Dat | |
| dc.contributor.author | Cios, Krzysztof J. | |
| dc.date.issued | 2015 | |
| dc.description.abstract | 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 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.format | Pp. 267-279 | |
| dc.identifier.uri | https://thuvienso.hoasen.edu.vn/handle/123456789/11026 | |
| dc.language.iso | en | |
| dc.source | Applied Soft Computing. Volume 35 | |
| dc.subject | One-class learning algorithm | |
| dc.subject | Outlier detection | |
| dc.subject | Anomaly detection | |
| dc.subject | Novelty detection | |
| dc.title | Rule-based OneClass-DS learning algorithm | |
| dc.type | Article |
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