A potential use of data envelopment analysis for the inverse classification problem

A potential use of data envelopment analysis for the inverse classification problem

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Article ID: iaor20041369
Country: United Kingdom
Volume: 30
Issue: 3
Start Page Number: 243
End Page Number: 248
Publication Date: Jun 2002
Journal: OMEGA
Authors:
Keywords: classification
Abstract:

We propose a methodology that uses data envelopment analysis (DEA) for solving the inverse classification problem. An inverse classification problem involves finding out how predictor attributes of a case can be changed so that the case can be classified into a different and more desirable class. For a binary classification problem and non-negative decision-making attributes, we show that under the assumption of conditional monotonicity, and convexity of classes, DEA can be used for inverse classification problem. We illustrate the application of our proposed methodology on hypothetical and real-life bankruptcy prediction data.

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