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| Data-Driven Multi-Criteria Decision Analysis× | Technika porządku preferencji przez podobieństwo do idealnego rozwiązania× | |
|---|---|---|
| Dziedzina | Podejmowanie decyzji | Podejmowanie decyzji |
| Rodzina | MCDM | MCDM |
| Rok powstania≠ | 2015 | 1981 |
| Twórca≠ | Multiple authors | Hwang, C. L., Yoon, K. |
| Typ≠ | Learning-based criteria weighting and aggregation | Distance-based (compromise) |
| Źródło pierwotne≠ | Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143. link ↗ | Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications — A State-of-the-Art Survey. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag DOI ↗ |
| Inne nazwy≠ | Data-Driven MCDA | — |
| Pokrewne≠ | 5 | 8 |
| Podsumowanie≠ | Data-Driven MCDA is a hybrid framework that integrates machine learning and statistical learning into traditional multi-criteria decision analysis. Instead of eliciting weights from expert judgment, it learns criteria importance from historical decision data, enabling more scalable and empirically grounded decision support. | TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateZbiór danych ↗ |
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