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Frontiers of Computer Science

, Volume 10, Issue 5, pp 845–855 | Cite as

Multi-label active learning by model guided distribution matching

  • Nengneng Gao
  • Sheng-Jun HuangEmail author
  • Songcan Chen
Research Article

Abstract

Multi-label learning is an effective framework for learning with objects that have multiple semantic labels, and has been successfully applied into many real-world tasks. In contrast with traditional single-label learning, the cost of labeling a multi-label example is rather high, thus it becomes an important task to train an effectivemulti-label learning model with as few labeled examples as possible. Active learning, which actively selects the most valuable data to query their labels, is the most important approach to reduce labeling cost. In this paper, we propose a novel approach MADM for batch mode multi-label active learning. On one hand, MADM exploits representativeness and diversity in both the feature and label space by matching the distribution between labeled and unlabeled data. On the other hand, it tends to query predicted positive instances, which are expected to be more informative than negative ones. Experiments on benchmark datasets demonstrate that the proposed approach can reduce the labeling cost significantly.

Keywords

multi-label learning batch mode active learning distribution matching 

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Supplementary material

11704_2016_5421_MOESM1_ESM.ppt (490 kb)
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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.College of Computer Science and Technology, Nanjing University of Aeronautics and AstronauticsCollaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

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