PAnDE: Averaged n-dependence estimators for positive unlabeled learning

Abstract

Traditional data mining algorithms are commonly based on fully labeled data, which is often practically difficult to obtain. In recent years, positive unlabeled (PU) learning has emerged as a useful technique to address this issue, which allows algorithms to learn from only positive and unlabeled data by relaxing the requirement for obtaining fully labeled data. Existing PU learning algorithms based on Bayesian classifiers, including PNB and PAODE, have been successfully applied to multiple classification problems. However, their empirical performance is affected by the attribute independence assumption. With the goal of effectively tackling positive unlabeled learning tasks with higher-leve..