A Data Envelopment Analysis-Based Approach for Data Preprocessing

Authors:
Parag C. Pendharkar
Affiliations:
-
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2005

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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.