Histogram-Based Asymmetric Relabeling for Learning from Only Positive and Unlabeled Data

Abstract

In this paper, we demonstrate how to use asymmetric data relabeling based on feature histograms as a pre-processing step for improving the overall classification performance of different classifiers in situations when only positive and unlabeled data is available. Additionally, this strategy can be used to identify with some level of confidence those data instances that should probably be labeled as positive. Moreover, this approach can be adapted to assess the quality of a given dataset, in terms of how many positive instances are not labeled. We examine our approach using synthetic data and demonstrate its applicability using real, publicly available data.