Solving one-class problem with outlier examples by SVM

Support Vector Data Description (SVDD) is an important algorithm for data description problem. SVDD uses only positive examples to learn a predictor whether an example is positive or negative. When a fraction of negative examples are available, the performance of SVDD is expected to be improved. SVDD-neg, as an extension of SVDD, learns a predictor with positive examples and a fraction negative ones. However, the performance of SVDD-neg becomes worse than SVDD in some cases when some negative examples are available. In this paper, a new algorithm “SVM-SVDD” is proposed, in which both Support Vector Machine (SVM) and SVDD are used to solve data description problem with negative examples. The experimental results illustrate that SVM-SVDD outperforms SVDD-neg on both training time and accuracy.