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Multi-label active learning by model guided distribution matching

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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.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 211106, China

    Nengneng Gao, Sheng-Jun Huang & Songcan Chen

Authors
  1. Nengneng Gao
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  2. Sheng-Jun Huang
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  3. Songcan Chen
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Corresponding author

Correspondence to Sheng-Jun Huang.

Additional information

Nengneng Gao received his BS from Henan University, China in 2014. He received the National Endeavor Fellowship and the Outstanding Student of Henan University in 2011, 2012 and 2013, respectively. Currently he is a master student at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. His research interests include machine learning and pattern recognition.

Sheng-Jun Huang received his BS and PhD degrees in computer science from Nanjing University in 2008 and 2014, respectively. He received the Microsoft Fellowship Award in 2011 and the Best Poster award at the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) in 2012. He joined PAttern Recognition and NEural Computing laboratory (PARNEC) as an assistant professor in 2014. His research interests include machine learning and pattern recognition.

Songcan Chen received the BS degree from Hangzhou University (now merged into Zhejiang University), China, the MS degree from Shanghai Jiao Tong University, China and the PhD degree from Nanjing University of Aeronautics and Astronautics (NUAA), China in 1983, 1985, and 1997, respectively. He joined in NUAA in 1986, and he has been a full-time Professor with the Department of Computer Science and Engineering since 1998. He has authored/coauthored over 170 scientific peer-reviewed papers and ever obtained Honorable Mentions of 2006, 2007 and 2010 Best Paper Awards of Pattern Recognition Journal respectively. His current research interests include pattern recognition, machine learning, and neural computing.

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Gao, N., Huang, SJ. & Chen, S. Multi-label active learning by model guided distribution matching. Front. Comput. Sci. 10, 845–855 (2016). https://doi.org/10.1007/s11704-016-5421-x

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  • DOI: https://doi.org/10.1007/s11704-016-5421-x

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