Attribute reduction and decision‐making model based on gray dual‐information

Abstract

Purpose

The purpose of this paper is to research attribute reduction and decision making by gray dual‐information, taking into account the attribute reduction of attribute decision unknown for the interval gray numbers.

Design/methodology/approach

The authors obtain the attribute weights considering the consistency of experts’ judgment matrixes and the decision matrixes with gray information. They propose some experts’ attribute reduction ideas based on interval gray numbers of rough set. With the help of experts’ decision information, they consider attribute uncertainty ratio and attribute value ratio to reduce attribute. Finally, a numerical example shows its feasibility.

Findings

Some experts’ attribute reduction ideas are proposed based on interval gray numbers of rough set. With the help of experts’ decision information, attribute uncertainty ratio and attribute value ratio to reduce attribute can be considered.

Originality/value

Attribute reduction is keeping classified information systems under the same conditions and deleting redundant and irrelevant or unimportant attributes in order to solve the problem of decision making. This paper considers the attribute reduction based on gray dual‐information.

Citation

Jie, Z. and Jian‐Jun, Z. (2012), "Attribute reduction and decision‐making model based on gray dual‐information", Grey Systems: Theory and Application, Vol. 2 No. 2, pp. 217-228. https://doi.org/10.1108/20439371211260180