When in a classification problem only samples of one class are easily accessible, this problem is called a one-class classification problem. Many standard classifiers, like backpropagation neural networks, fail on this data. Some other classifiers, like k-means clustering or nearest neighbor classifier can be applied after some minor changes. In this paper we focus on the support vector data description classifier, which is especially constructed for one-class classification. But this method appears to be sensitive to scaling of the individual features of the dataset. We show that it is possible to improve its performance by adequate scaling of the feature space. Some results will be shown on artificial dataset and handwritten digits dataset.
|Title of host publication||Proceedings ASCI 2002|
|Editors||EF Deprettere, A Belloum, JWJ Heijnsdijk, F van der Stappen|
|Place of Publication||Delft|
|Number of pages||8|
|Publication status||Published - 2002|
|Event||8th Annual Conf. of the Advanced School for Computing and Imaging, Lochem - Delft|
Duration: 19 Jun 2002 → 21 Jun 2002
|Conference||8th Annual Conf. of the Advanced School for Computing and Imaging, Lochem|
|Period||19/06/02 → 21/06/02|
- conference contrib. non-refer.
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