A new algorithm of similarity measuring for multi-experts’ qualitative knowledge based on outranking relations in case-based reasoning methodology

Authors:
Hui Li;Xiang-Yang Li;Jie Gu
Affiliations:
Harbin Institute of Technology, Harbin, School of Management, Heilongjiang Province, China;Harbin Institute of Technology, Harbin, School of Management, Heilongjiang Province, China;School of Software, Tsinghua University, Beijing, China
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

Qualitative knowledge reasoning is a key content in knowledge science. Case-based reasoning is one of the main reasoning methodologies in artificial intelligence. Outranking relation methods, called ELECTRE and others, have been developed. In this research, a new algorithm of similarity measuring for qualitative problems in the presence of multiple experts based on outranking relations in case-based reasoning was proposed. Strict preference, weak preference, and indifference relations were introduced to formulate imprecision, uncertainty, incompleteness knowledge from multi-experts. Case similarities were integrated through aggregating house on the foundation of outranking relations. Experiments indicated that the new algorithm got accordant outcome with traditional quantitative similarity mode but extended its application range.