Neural Computing and Applications

, Volume 20, Issue 7, pp 1061–1074 | Cite as

An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network

  • Furao Shen
  • Hui Yu
  • Keisuke Sakurai
  • Osamu Hasegawa
Original Article


An incremental online semi-supervised active learning algorithm, which is based on a self-organizing incremental neural network (SOINN), is proposed. This paper describes improvement of the two-layer SOINN to a single-layer SOINN to represent the topological structure of input data and to separate the generated nodes into different groups and subclusters. We then actively label some teacher nodes and use such teacher nodes to label all unlabeled nodes. The proposed method can learn from both labeled and unlabeled samples. It can query the labels of some important samples rather than selecting the labeled samples randomly. It requires neither prior knowledge, such as the number of nodes, nor the number of classes. It can automatically learn the number of nodes and teacher vectors required for a current task. Moreover, it can realize online incremental learning. Experiments using artificial data and real-world data show that the proposed method performs effectively and efficiently.


Semi-supervised learning Active learning Online incremental learning Self-organizing incremental neural network 



This work was supported in part by the Fund of the National Natural Science Foundation of China (Grant No. 60975047, 60723003, 60721002), 973 Program (2010CB327903), and Jiangsu NSF grant (\#BK2009080). This study was also supported in part by the New Energy and Industrial Technology Development Organization (NEDO) of Japan.


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Furao Shen
    • 1
    • 2
  • Hui Yu
    • 1
    • 2
  • Keisuke Sakurai
    • 3
  • Osamu Hasegawa
    • 3
  1. 1.The State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  2. 2.Jiangyin Information Technology Research InstituteNanjing UniversityNanjingPeople’s Republic of China
  3. 3.Imaging Science and Engineering LaboratoryTokyo Institute of TechnologyTokyoJapan

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