A data envelopment analysis-based approach for data preprocessing

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35 Scopus citations


In this paper, we show how the data envelopment analysis (DEA) model might be useful to screen training data so a subset of examples that satisfy monotonicity property can be identified. Using real-world health care and software engineering data, managerial monotonicity assumption, and artificial neural network (ANN) as a forecasting model, we illustrate that DEA-based data screening of training data improves forecasting accuracy of an ANN.

Original languageEnglish (US)
Pages (from-to)1379-1388
Number of pages10
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number10
StatePublished - Oct 2005

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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