A new method for positive and unlabeled learning with privileged information

Absolute Value Inequality SVM for the PU Learning Problem

“…This type of method consists of two steps: (1) identifying reliable negative examples from the unlabeled examples, and (2) learning based on the labeled positive examples and the identified reliable negative examples. The representative methods in this category include S-EM [16], PEBL [17], Roc-SVM [18], SPUPIL [19], and KNN-SVM [20]. S-EM [16] uses the Spy technique to obtain some reliable negative examples from the unlabeled set and then runs the Expectation-Maximization (EM) algorithm to construct the final classifier.…”

“…Roc-SVM [18] builds prototypes for labeled and unlabeled examples based on Rocchio classification, counts unlabeled examples closer to the unlabeled prototypes as reliable negative examples, and then applies the SVM algorithm to learn the classifier. SPUPIL [19] first takes the intersection of reliable negative examples extracted by the Spy [16] and Rocchio [18] methods as the final reliable negative examples, and then incorporates similarity weights and privileged information into the learning to extend the standard ranking SVM [26] to obtain a more accurate classifier. KNN-SVM [20] sorts the unlabeled examples by calculating the sum of the cosine similarities with the nearest k positive examples to select reliable negative examples in the first step.…”

“…The third and most widely used category is the two-step method. This method selects potential negative examples from the unlabeled set and then constructs a classifier using both positive and identified negative examples [16][17][18][19][20]. Many algorithms of this category underutilize the remaining unlabeled data, resulting in limited classification performance.…”