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Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria
János Abonyi1, Tímea Czvetkó1, Zsolt T Kosztyán2
1MTA-PE "Lendület" Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary.
Plos One
|February 25, 2022
Summary
This study enhances the Promethee-GAIA multicriteria decision method by introducing three techniques to improve interpretability and identify relationships between criteria, especially for complex problems with many factors.
Area of Science:
- Decision Sciences
- Operations Research
- Data Analysis
Background:
- The Promethee-GAIA method is a multicriteria decision support tool.
- Principal Component Analysis (PCA) biplots in Promethee-GAIA lack clarity with numerous criteria.
- Interpreting criterion influence and relationships is challenging.
Purpose of the Study:
- To improve the Promethee-GAIA method for enhanced interpretability.
- To reveal characteristic inner relationships between criteria.
- To address limitations in visualizing numerous criteria.
Main Methods:
- Introduced three techniques: Principal Factoring with Rotation and Communality Analysis (P-PFA), Sparse PCA integration with Promethee II (P-sPCA), and Sum of Ranking Differences (P-SRD).
- Applied methods to an Industry 4.0 readiness dataset (I4.0+).
- Focused on eliminating redundant criteria and exploring criterion similarities.
Main Results:
- The proposed methods offer improved interpretability for multicriteria ranking problems.
- Redundant criteria can be identified and eliminated.
- Factor structures and criterion relationships become clearer.
Conclusions:
- The P-PFA, P-sPCA, and P-SRD methods enhance the Promethee-GAIA approach for complex decision-making.
- These techniques are valuable for handling a large number of criteria.
- Improved visualization and understanding of criterion influence are achieved.
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