Boosted SVM with active learning strategy for imbalanced data

Maciej Zięba*, Jakub M. Tomczak

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


In this work, we introduce a novel training method for constructing boosted Support Vector Machines (SVMs) directly from imbalanced data. The proposed solution incorporates the mechanisms of active learning strategy to eliminate redundant instances and more properly estimate misclassification costs for each of the base SVMs in the committee. To evaluate our approach, we make comprehensive experimental studies on the set of 44 benchmark datasets with various types of imbalance ratio. In addition, we present application of our method to the real-life decision problem related to the short-term loans repayment prediction.

Original languageEnglish
Pages (from-to)3357-3368
Number of pages12
JournalSoft Computing
Issue number12
Publication statusPublished - 1 Dec 2015
Externally publishedYes


  • Active learning
  • Boosted SVM
  • Imbalanced data


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