NEATER: filtering of over-sampled data using non-cooperative game theory

B. A. Almogahed, I. A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


In this paper, we present a method for the filteriNg of ovEr-sampled dAta using non-cooperaTive gamE theoRy (NEATER) to address the imbalanced data problem. Specifically, the problem is formulated as a non-cooperative game where all the data are players and the goal is to uniformly and consistently label all of the synthetic data created by any over-sampling technique. The proposed algorithm does not require any prior assumptions and selects representative synthetic instances while generating a very small number of noisy data. We present extensive experimental results over a large collection of datasets using three different classifiers to demonstrate the advantages of our method.

Original languageEnglish (US)
Pages (from-to)3301-3322
Number of pages22
JournalSoft Computing
Issue number11
StatePublished - Nov 24 2015


  • Classification
  • Imbalanced data
  • Sampling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology


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