Active Learning on Heterogeneous Information Networks - A Multi-armed Bandit ApproachEI
Active learning exploits inherent structures in the unlabeled data to minimize the number of labels required to train an accurate model. It enables effective machine learning in applications with high labeling cost, such as document classification and drug response prediction. We investigate active learning on heterogeneous information networks, with the objective of obtaining accurate node classifications while minimizing the number of labeled nodes. Our proposed algorithm harnesses a multi-armed bandit (MAB) algorithm to determine n...更多
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ICDM, pp. 1350-1355, 2018.