A Survey of Data Acquisition Models and Methods for Uncertainty Decision-Making Analysis
Uncertainty decision-making entails making choices in contexts where outcomes are ambiguous or challenging to pre-dict precisely.This paper examines four approaches to data acquisition in such scenarios:①Small dataset;②Open dataset;③Sentiment analysis and remote sensing analysis from the perspective of data mining;④Questionnaire survey method from the perspective of the Internet.Initially,the paper reviews these four methods,outlining their key features and historical evolution.Subsequently,it delves into their applications in uncertainty decision-making,supported by illustrative case studies.Further-more,the paper identifies limitations of these data acquisition techniques and suggests potential directions for future research.In conclusion,this paper offers both theoretical insights and practical guidelines for the study of uncertainty decision-making,aiming to contribute to advancements in artificial intelligence and decision science,ultimately assisting decision-makers in mak-ing more informed and accurate decisions.
uncertainty decision-makingdata acquisitionsmall data setopen data setdata mininginternet
NETL
NSTL
万方数据
