International Journal of Computational Intelligence Systems

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Volume 14, Issue 1, 2021, Pages 1022 - 1033

Regret Theory-Based Case-Retrieval Method with Multiple Heterogeneous Attributes and Incomplete Weight Information

Authors
Kai Zhang1, Ying-Ming Wang2, *, Jing Zheng3
1College of Information and Intelligent Transportation, Fujian Chuanzheng Communications College, Fujian, 350007, P.R. China
2Business School, Wuchang University of Technology, Hubei, 430223, P.R. China
3College of Electronics and Information Science, Fujian Jiangxia University, Fujian, 350108, P.R. China
*Corresponding author. Email: msymwang@hotmail.com
Corresponding Author
Ying-Ming Wang
Received 27 December 2020, Accepted 18 February 2021, Available Online 3 March 2021.
DOI
10.2991/ijcis.d.210223.002How to use a DOI?
Keywords
Case retrieval; Regret theory; Multiple heterogeneous attributes; Incomplete weight information; Mathematical programming
Abstract

Case retrieval is a crucial step in case-based reasoning (CBR), which is related to decision-making effectiveness. To improve decision support, CBR usually calculates case similarity and evaluates utility. However, the psychological behavior of decision makers is seldom considered in case retrieval. This paper proposes a novel case-retrieval method that deals with multiple heterogeneous attributes and incomplete weight information based on regret theory (RT). First, we define the function of the perceived utility based on attribute similarity and RT. Next, a mathematical programming model is constructed to determine the attribute weights based on linear programming technique for multidimensional analysis of preference (LINMAP). Based on this, we can calculate the perceived utility and determine a set of similar historical cases. Furthermore, the utilities of the evaluated attributes are calculated based on RT and LINMAP. Subsequently, we compute the comprehensive utilities of similar historical cases and obtain the ranking order of similar historical cases. Thus, the most suitable historical case is obtained. Finally, a case study of a gas explosion is conducted to illustrate the use of the proposed method.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
1022 - 1033
Publication Date
2021/03/03
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.210223.002How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Kai Zhang
AU  - Ying-Ming Wang
AU  - Jing Zheng
PY  - 2021
DA  - 2021/03/03
TI  - Regret Theory-Based Case-Retrieval Method with Multiple Heterogeneous Attributes and Incomplete Weight Information
JO  - International Journal of Computational Intelligence Systems
SP  - 1022
EP  - 1033
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210223.002
DO  - 10.2991/ijcis.d.210223.002
ID  - Zhang2021
ER  -