Abstract
In this paper, we illustrate how data envelopment analysis (DEA) can be used to aid interactive classification. We assume that the scoring function for the classification problem is known. We use DEA to identify difficult to classify cases from a database and present them to the decision-maker one at a time. The decision-maker assigns a class to the presented case and based on the decision-maker class assignment, a tradeoff cutting plane is drawn using the scoring function and decision-maker’s input. The procedure continues for finite number of iterations and terminates with the final discriminant function. We also show how a hybrid DEA and mathematical programming approach can be used when user interaction is not desired. For non-interactive case, we compare a hybrid DEA and mathematical programming based approach with several statistical and machine learning approaches, and show that the hybrid approach provides competitive performance when compared to the other machine learning approaches.
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Notes
More specifically, this is a basic BCC (Banker et al. 1984) input minimizing model with multiple inputs and one output that takes a constant value of unity for all accept class examples. The vector x is assumed to represent m-dimensional inputs. For BCC input model please see http://www.deazone.com and select BCC input under model tab.
This is a basic BCC output maximizing model with multiple outputs and one input that takes a constant value of unity for all reject class examples. The vector x is assumed to represent m-dimensional outputs. For BCC output model please see http://www.deazone.com and select BCC output under model tab.
Because such a line will be below the accept class frontier.
Because such a line will be above the reject class frontier.
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Pendharkar, P.C., Troutt, M.D. Interactive classification using data envelopment analysis. Ann Oper Res 214, 125–141 (2014). https://doi.org/10.1007/s10479-012-1091-8
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DOI: https://doi.org/10.1007/s10479-012-1091-8