Usporedite metode

	Podatkovno utemeljena višekriterijska analiza odlučivanja	Tehnika za redoslijed preferencija po sličnosti s idealnim rješenjem
Područje	Donošenje odluka	Donošenje odluka
Obitelj	MCDM	MCDM
Godina nastanka	2015	1981
Tvorac	Multiple authors	Hwang, C. L., Yoon, K.
Vrsta	Learning-based criteria weighting and aggregation	Distance-based (compromise)
Temeljni izvor	Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.	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
Drugi nazivi	Data-Driven MCDA	—
Srodne	5	8
Sažetak	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.