Dynamic sampling approach to training neural networks for multiclass imbalance classification

Minlong LIN, Ke TANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

142 Citations (Scopus)


Class imbalance learning tackles supervised learning problems where some classes have significantly more examples than others. Most of the existing research focused only on binary-class cases. In this paper, we study multiclass imbalance problems and propose a dynamic sampling method (DyS) for multilayer perceptrons (MLP). In DyS, for each epoch of the training process, every example is fed to the current MLP and then the probability of it being selected for training the MLP is estimated. DyS dynamically selects informative data to train the MLP. In order to evaluate DyS and understand its strength and weakness, comprehensive experimental studies have been carried out. Results on 20 multiclass imbalanced data sets show that DyS can outperform the compared methods, including pre-sample methods, active learning methods, cost-sensitive methods, and boosting-type methods. © 2012 IEEE.
Original languageEnglish
Article number6449324
Pages (from-to)647-660
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
Early online date4 Feb 2013
Publication statusPublished - Apr 2013
Externally publishedYes


  • Cost-sensitive learning
  • dynamic sampling
  • multiclass imbalance learning
  • multilayer perceptrons


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