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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.
Index Terms- Data envelopment analysis, artificial neural networks, data preprocessing.

P. C. Pendharkar, "A Data Envelopment Analysis-Based Approach for Data Preprocessing," in IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 1379-1388, 2005.
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