Multi-Attribute Decision Making Based on Attribute Importance Degree and Case-Based Reasoning

Zhong Wu; Ruixia Yan

doi:10.2478/cait-2013-0038

Abstract

To tackle a multi-attribute decision making problem, rough set and casebased reasoning are often combined. However, the reduction in a rough set is always complex. In this paper we provide a new relative importance measure about the unitary attributes values by ranking the relative importance of the attributes in the rough set theory. A new rough set model based on ranking the relative importance of the attributes is built and its properties are studied. Then unitary attributes values are utilized to compute the similarity of rules in case-based reasoning, for there might be incompletely match or miss values. A new multiattribute decision making based on case-based reasoning and a rough set based on the ranking relative importance of the attributes is constructed, which obtains rules, avoiding reduction and rule extraction.

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Multi-Attribute Decision Making Based on Attribute Importance Degree and Case-Based Reasoning

Abstract

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