Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
495166 | Applied Soft Computing | 2015 | 13 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Dat T. Nguyen, Krzysztof J. Cios,