A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
This work addresses a semi-supervised learning challenge for scenarios with limited labeled data, but it is incremental as it builds on existing ensemble and SVM techniques.
The paper tackles the problem of binary classification with only positive and unlabeled data (PU learning) by proposing an ensemble of SVM models trained on bootstrap resamples to increase robustness against label noise. It shows marginal improvement over existing methods in standard PU learning and significant improvement in PU learning with false positives on benchmark datasets.
We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap resamples of the training data for increased robustness against label noise. The approach can be considered in a bagging framework which provides an intuitive explanation for its mechanics in a semi-supervised setting. We compared our method to state-of-the-art approaches in simulations using multiple public benchmark data sets. The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives. Our approach shows a marginal improvement over existing methods in the second setting and a significant improvement in the third.