Gravity Oriented One-Class Classifier Based on Support Vector Data Descriptor

In this paper, a one-class classifier based on the Support Vector Data Descriptor (SVDD) is proposed. In SVDD, even outlier samples which are out of the decision boundary, are affecting the boundary. This increases the error of the classifier. In the proposed classifier, decision boundary is determined by all of the samples through a gravity oriented approach. In this way, two classifier is proposed which in one of them knowledge about outliers are also considered. The optimization problem of the proposed method is convex and can be used with the kernel methods. Experiments on the behavior of the proposed classifier regarding changes of the parameters were done. Comparing results of experiments with results of SVDD and Density Induced SVDD shows that the proposed method can decrease the effects of outliers.