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A review of multi-instance learning research

TIAN Yingjie1,2   XU Dongkuan1   ZHANG Chunhua3,*   

  1. 1. Research Center on Fictitious Economy and Data Science,  the Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China; 2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; 3. School of Information, Renmin University of China, Beijing 100872, China
  • Received:2017-09-30 Online:2018-06-15 Published:2018-06-15

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Abstract:

Multi-instance learning is  a special kind of machine learning problem, has received extensive attention and been researched on in recent years. Many different types of multi-instance learning algorithms have been proposed to deal with practical problems in various fields. This paper reviews the algorithm research and application of multi-instance learning in detail, introduces various background assumptions， and introduces multi-instance learning from three aspects: instance level, bag level, and embedded space. Finally we provide the algorithm extensions  and major applications in several areas.

Key words: multi-instance learning, classification problem, bag, support vector machine, deep learning