Unlabeled-unlabeled (UU) classification (du Plessis et al. 2013) allows us to train a binary classifier from two sets of unlabeled data with different class priors. In this paper, we go beyond this scenario and try to train a binary classifier only from a single set of unlabeled data. Our key idea is to iteratively perform UU classification: We initially split the original single unlabeled dataset into two disjoint datasets and perform UU classification. We then split the original unlabeled dataset in a different way based on the obtained classifier, perform UU classification, and repeat this process until convergence. We numerically show that the classification accuracy tends to be improved over iterations. Finally, we apply our iterative UU classification method to a realworld drowsiness prediction problem and demonstrate its usefulness.
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