Open AccessMethodological foundations of scaling in modern Measurement Theory. Classification of measurement scales and their application under uncertainty based on Bayesian Intelligent Technologies
Author(s) -
Svetlana V. Prokopchina
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1703/1/012003
Subject(s) - intellectualization , computer science , data science , big data , relevance (law) , bayesian probability , data mining , variety (cybernetics) , measurement uncertainty , machine learning , artificial intelligence , industrial engineering , engineering , mathematics , psychology , statistics , political science , law , psychotherapist
Measurement theory is currently being actively developed due to its relevance in modern information technologies that implement the principles and algorithms of DATA SCIENCE, BIG DATA, Internet of Things, Business Intelligence, and DATA MINING. One of the most important applications of measurement theory methods and tools is implemented in artificial intelligence technologies. This is due to the need to use a variety of measurement data in these technologies. The conditions for obtaining such measurement information are associated with uncertainty, which necessitates the use of special methods and tools for obtaining measurement data. This, in turn, determines the need to develop and apply new types of scales focused on uncertainty conditions. The article offers a classification of measurement scales for the implementation of classical and intelligent measurement algorithms. The principles of implementing scales with dynamic constraints for measurement intellectualization based on Bayesian intelligent technologies are considered. Examples of using smart measurement scales are given.