Vertaile menetelmiä

	Data-Driven Multi-Criteria Decision Analysis	ELECTRE I	Yksinkertainen additiivinen painotus
Tieteenala	Päätöksenteko	Päätöksenteko	Päätöksenteko
Menetelmäperhe	MCDM	MCDM	MCDM
Syntyvuosi	2015	1968	1967
Kehittäjä	Multiple authors	Roy, B.	Fishburn, P. C.
Tyyppi	Learning-based criteria weighting and aggregation	Concordance–discordance (crisp outranking)	Additive utility (linear)
Alkuperäislähde	Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.	Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Revue Française d'Informatique et de Recherche Opérationnelle	Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research
Rinnakkaisnimet	Data-Driven MCDA	—	—
Liittyvät	5	8	8
Tiivistelmä	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.	ELECTRE (ELECTRE I — ELimination Et Choix Traduisant la REalité) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	SAW (Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.