A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
Abstract
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.
Cite
@article{arxiv.1402.3144,
title = {A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models},
author = {Marc Claesen and Frank De Smet and Johan A. K. Suykens and Bart De Moor},
journal= {arXiv preprint arXiv:1402.3144},
year = {2015}
}
Comments
34 pages, 6 figures, 6 tables. Accepted for publication in Neurocomputing: Special Issue on Advances in Learning with Label Noise