A novel classification algorithm based on incremental semi-supervised support vector machine

Fei Gao, Jingyuan Mei, Jinping Sun, Jun Wang, Erfu Yang, Amir Hussain

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
184 Downloads (Pure)


For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.
Original languageEnglish
Article numbere0135709
Number of pages19
JournalPLOS One
Issue number8
Early online date14 Aug 2015
Publication statusPublished - 14 Aug 2015


  • incremental learning
  • semi-supervised learning
  • support vector machine
  • concept drift
  • changing environment

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