Broaden the minority class space for decision tree induction using antigen-derived detectors

Xusheng Ai, Jian Wu, Zhiming Cui, Victor S. Sheng

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

To deal with lack of density over imbalanced datasets, a Negative Selection Over-Sampling Technology (NSOTE) is proposed. NSOTE is based on a negative selection mechanism of our human immune system. It generates antigen-derived detectors of majority class examples to enrich the decision regions of the space of minority class. Meanwhile, through learning the density distribution of minority class examples, NSOTE eliminates the noise detectors that deviate from the minority class space. Our experimental results show that our NSOTE can achieve better performance than existing resampling methods.

Original languageEnglish
Pages (from-to)196-205
Number of pages10
JournalKnowledge-Based Systems
Volume137
DOIs
StatePublished - Dec 1 2017

Keywords

  • Decision tree
  • Imbalance learning
  • Negative selection
  • Resampling

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