One-class SVM extracts initial negative example set In figure 2, plus signs, plus signs with circle and circles denote positive examples, potential positive examples and unlabeled examples respectively. The points covered by ellipse are negative examples set N0 and the line is classification hyperplane. One-class SVM is utilized to extract the initial negative examples. Give a percentage of negative examples, such as 10 percent, it can draw an initial decision boundary to cover most of the positive and unlabeled data. The data points not covered by the decision boundary can be regarded as negative data points because these data points are far from the major positive set.
Chen et al. BMC Genomics 2010 11(Suppl 2):S11 doi:10.1186/1471-2164-11-S2-S11