@inproceedings{b173118fb87f45ec8d81bd1089a94bb8,
title = "Active Learning on Heterogeneous Information Networks: A Multi-armed Bandit Approach",
abstract = "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 network structures that identify the most important nodes to the classification task, accounting for node types and without assuming label assortativity. Evaluations on real-world network classification tasks demonstrate that our algorithm outperforms existing methods independent of the underlying classification model.",
keywords = "Active learning, Heterogeneous information networks, Multi armed bandit",
author = "Doris Xin and Ahmed El-Kishky and De Liao and Brandon Norick and Jiawei Han",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 18th IEEE International Conference on Data Mining, ICDM 2018 ; Conference date: 17-11-2018 Through 20-11-2018",
year = "2018",
month = dec,
day = "27",
doi = "10.1109/ICDM.2018.00184",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1350--1355",
booktitle = "2018 IEEE International Conference on Data Mining, ICDM 2018",
address = "United States",
}