{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90788"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90788","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A multi-armed bandit approach for batch mode active learning on information networks","abstract":"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.","abstract_html":"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. 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Evaluations of MABAL on real world network classification tasks demonstrate that it achieves performance gains over existing methods independent of the underlying classification model.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, De Liao, accepted the attached license on 2016-04-18 at 21:29.","The student, De Liao, submitted this Thesis for approval on 2016-04-18 at 21:30.","This Thesis was approved for publication on 2016-04-19 at 11:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9322 on 2016-07-07 at 13:50:02","Made available in DSpace on 2016-07-07T20:27:42Z (GMT). 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