A data envelopment analysis-based approach for data preprocessing Parag C. Pendharkar   View Paper (PDF)   Return to October 2005, Volume 17, Issue 10 Abstract 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. ©2006 Association for Computing Machinery