Bankruptcy Prediction with Rough Sets

Cor Bioch and Viara Popova

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

Abstract: The bankruptcy prediction problem can be considered an or dinal classification problem. The classical theory of Rough Sets describes objects by discrete attributes, and does not take into account the order- ing of the attributes values. This paper proposes a modification of the Rough Set approach applicable to monotone datasets. We introduce re- spectively the concepts of monotone discernibility matrix and monotone (object) reduct. Furthermore, we use the theory of monotone discrete functions developed earlier by the first author to represent and to com- pute decision rules. In particular we use monotone extensions, decision lists and dualization to compute classification rules that cover the whole input space. The theory is applied to the bankruptcy prediction problem.

Keywords: attributes selection; bankruptcy prediction; decision rules; ordinal classification; rough sets (search for similar items in EconPapers)
JEL-codes: C53 M M11 R4 (search for similar items in EconPapers)
Date: 2001-02-22
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Citations: View citations in EconPapers (1)

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