Supervised Learning Method of Parallel Classified Hesitant Information and Its Application in the Smart Home System Selection

In the group decision-making process, experts and decision makers can sometimes provide subjective evaluation information with classification labels. To effectively deal with it and make a reasonable decision, two key issues should be addressed first, which include the information representation and...

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Veröffentlicht in:IEEE transactions on engineering management Jg. 71; S. 1 - 13
Hauptverfasser: Liu, Man, Zhou, Wei, Xu, Zeshui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Classification
Classification algorithms
classification probability information
Decision making
Decision making units
Fuzzy sets
group classification consensus
Hafnium
Labels
Mathematical models
parallel classified hesitant fuzzy set
Smart buildings
smart home system
Smart homes
Smart houses
Supervised learning
Supervised learning algorithm
ISSN:0018-9391, 1558-0040
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Zusammenfassung:In the group decision-making process, experts and decision makers can sometimes provide subjective evaluation information with classification labels. To effectively deal with it and make a reasonable decision, two key issues should be addressed first, which include the information representation and consensus classification model in the above uncertain information environment. To do so, this paper extends the hesitant fuzzy set (HFS), which is a hot and effective presentation tool in recent years, to the classification HFS (CHFS), the parallel HFS, and the parallel CHFS. Thus, we can mathematically present three types of evaluation information with classification labels or parallel characteristics in the consensus classification process. Then, to model the parallel classified hesitant fuzzy information and help further consensus classification, this paper proposes a supervised learning algorithm and proves its generalization and optimization. In addition, based on the supervised learning algorithm and the obtained classification probability information, we develop a consensus classification method in the parallel classified hesitant fuzzy environment to derive the optimal consensus classification results. Finally, this paper applies the proposed algorithm and methods to a real example of smart home system selection, which can show their rationality and feasibility.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2024.3413786