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Heterogeneous Multi-attribute Case Retrieval Method Based on Group Decision Making Using Incomplete Weight Information by Kai Zhang; Jing Zheng; Ying-Ming Wang is a Computer Science article available to read on EtoBox.

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Case-based reasoning (CBR) is one of the most popular methods used in emergency decision making (EDM). Case retrieval plays a key role in EDM processes based on CBR and usually functions by retrieving similar historical cases using similarity measurements. Decision makers (DMs), thus, choose the most appropriate historical cases. Although uncertainty and fuzziness are present in the EDM process, in-depth research on these issues is still lacking. In this study, a heterogeneous multi-attribute case retrieval method based on group decision making (GDM) with incomplete weight information is developed. First, the case similarities between historical and target cases are calculated, and a set of similar historical cases is constructed. Six formats of case attributes are considered, namely crisp numbers, interval numbers, linguistic variables, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (NNs) and interval-valued NNs. Next, the evaluation information from the DMs is expressed using single-valued NNs. Additionally, the evaluation utilities of similar historical cases are obtained by aggregating the evaluation information. The comprehensive utilities of similar historic

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Author

Kai Zhang; Jing Zheng; Ying-Ming Wang

Publisher

IOS Press

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)