Focused PU learning from imbalanced data
For practitioners dealing with imbalanced positive-unlabeled data (e.g., fraud detection, gene identification), this method offers improved classification accuracy over existing approaches.
The paper proposes a new PU learning method for highly imbalanced datasets, achieving state-of-the-art performance under SCAR and SAR labeling mechanisms, and demonstrating effectiveness in financial misstatement detection.
We propose a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets. Many real-world problems, such as disease gene identification, targeted marketing, fraud detection, and recommender systems, are hard to address with machine learning methods, due to limited labeled data. Often, training data comprises positive and unlabeled instances, the latter typically being dominated by negative, but including also several positive instances. While PU learning is well-studied, few methods address imbalanced settings or hard-to-detect positive examples that resemble negative ones. Our approach uses a focused empirical risk estimator, incorporating both positive and unlabeled examples to train binary classifiers. Empirical evaluations demonstrate state-of-the-art performance on imbalanced datasets under two labeling mechanisms - selecting positives completely at random (SCAR) and selecting at random (SAR). Beyond these controlled experiments, we demonstrate the value of the proposed method in the real-world application of financial misstatement detection.