Methodology and Multicriteria Algorithm for Group Decision Support in Classification Problems
Affiliation:
1. Division of Mathematics-Informatics and Statistics-Econometrics, Department of Economics, National and Kapodistrian University of Athens, GREECE
Abstract
In this work, a Group Decision methodology and algorithm for small collaborating teams is introduced. It is based on a multicriteria algorithm for classification decisions, where aggregation of member preferences is executed at the parameter level. The algorithm applies to relatively well-structured problems guided by a process facilitator. Initially, a set of parameters is proposed by the facilitator to the group and next group members evaluate the proposed parameter set and express their preferences in numeric or linguistic format. Individual preferences are aggregated by appropriate operators, and a set of group parameter values is generated, which is used as input for the classification algorithm. NeXClass multicriteria classification algorithm is used for the classification of alternatives, initially at a training set of alternatives and later at the entire set. Finally, group members evaluate results, and consensus, as well as satisfaction metrics, are calculated. In case of a low acceptance level, problem parameters are reviewed by the facilitator, and the aggregation phase is repeated. The methodology is a valid approach for group decision problems and can be utilized in numerous business environments. The algorithm can be also utilized by software agents in multiagent environments for automated decision-making, given the large volume of agent-based decision-making in various settings today.
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Subject
General Computer Science
Reference13 articles.
1. Wang, X., Xu, Z., Su, S. F., & Zhou, W., A comprehensive bibliometric analysis of uncertain group decision making from 1980 to 2019. Information Sciences, 2021, vol.547, pp.328-353. 2. Basílio, M. P., Pereira, V., Costa, H. G., Santos, M., & Ghosh, A., A systematic review of the applications of multi-criteria decision aid methods (1977–2022). Electronics, 2022, vol.11(11), 1720. 3. Jameson, A., Willemsen, M. C., & Felfernig, A., Individual and Group Decision Making and Recommender Systems. In Recommender Systems Handbook (pp. 789-832). 2022, New York, NY: Springer US. 4. Heradio, R., Fernandez-Amoros, D., Cerrada, C., & Cobo, M. J., Group decision-making based on artificial intelligence: A bibliometric analysis. Mathematics, 2020, vol.8(9), 1566. 5. Cai, Y., Jin, F., Liu, J., Zhou, L., & Tao, Z., A survey of collaborative decision-making: Bibliometrics, preliminaries, methodologies, applications and future directions. Engineering Applications of Artificial Intelligence, 2023, vol.122, 106064.
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