Name
Abstract | ||
|---|---|---|
This paper introduces a novel method for forming efficient one-class classifier ensembles. A common problem in one-class classification is a complex structure of the target class, which often leads to creation of a too expanded decision boundary. We propose to employ a clustering step in order to partition the target class into atomic subsets and using these as input for one-class classifiers. By this, we are able to detect sub-structures in the target concept. Additionally, to increase the diversity and robustness of our method weighted one-class classifiers are used. We introduce a novel scheme for calculating weights for training objects. Membership functions, obtained from the fuzzy clustering, are used to initialize the weighted classifiers. Based on the results of a number of computational experiments we show that the proposed method outperforms both the single one-class methods, as well as popular one-class ensembles. Other advantages are the highly parallel structure of the proposed solution, which facilitates parallel training and execution stages, and the relatively small number of control parameters. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.489 | Pattern Recognition |
Keywords | DocType | ISSN |
fuzzy set theory,pattern classification,atomic subsets,complex structure,decision boundary,fuzzy clustering,fuzzy feature space partitioning,highly parallel structure,membership functions,weighted one-class classifier ensemble | Conference | 1051-4651 |
Citations | PageRank | References |
1 | 0.35 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bartosz Krawczyk | 1 | 721 | 60.97 |
| Michał Woźniak | 2 | 213 | 24.64 |
| Boguslaw Cyganek | 3 | 145 | 24.53 |