A multi-armed bandit approach for batch mode active learning on information networks

Active learning;Heterogeneous information networks;Multi-armed bandit

Liao, De ; Han ; Jiawei

关键词: Active learning;    Heterogeneous information networks;    Multi-armed bandit;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/90788/LIAO-THESIS-2016.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

We propose an adaptive batch mode active learning algorithm, MABAL (Multi-Armed Bandit for Active Learning), for classification on heterogeneous information networks. Observing the parallels between active learning and multi-armed bandit (MAB), we base MABAL on an existing combinatorial MAB algorithm to combine simple strategies to generate query batches. MABAL employs a novel error expectation measure for network classification that does not assume assortativity as MAB reward feedback to determine the most fit strategy for the given task. We provide a preliminary optimality analysis of MABAL based on performance bounds for combinatorial MAB. A case study illustrates that MABAL not only converges quickly to the optimal strategy but also provides insight into the functional roles of the different node types. Evaluations of MABAL on real world network classification tasks demonstrate that it achieves performance gains over existing methods independent of the underlying classification model.