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PCA svēršana×Adaptīvu standartizētu intervālu pamatota alternatīvu ranžēšanas tehnika×
NozareLēmumu pieņemšanaLēmumu pieņemšana
SaimeMCDMMCDM
Izcelsmes gads19012024
AutorsPearson, K.Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.
TipsWeight_Objective (PCA variance explained, eigenvector-based)Two-level standardization + ideal/anti-ideal utility (β-anchored)
PirmavotsPearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine DOI ↗Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. (2024). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft Computing DOI ↗
Citi nosaukumi
Saistītās88
KopsavilkumsPCA-WEIGHT (PCA Weighting — Principal Component Analysis based objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pearson, K. in 1901. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) is a ranking multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGateSalīdzināt metodes: PCA-WEIGHT · ARTASI. Izgūts 2026-06-18 no https://scholargate.app/lv/compare