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

Liao, De

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https://hdl.handle.net/2142/90788 Copy

Description

Title

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

Author(s)

Liao, De

Issue Date

2016-04-19

Director of Research (if dissertation) or Advisor (if thesis)

Han, Jiawei

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Active learning
• Heterogeneous information networks
• Multi-armed bandit

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.

Graduation Semester

2016-05

Type of Resource

text

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http://hdl.handle.net/2142/90788

Copyright and License Information

Copyright 2016 De Liao

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