Generative Models in the Problem of Evaluating the Efficiency of Computer Algorithms

• Published in: Cybernetics and systems analysis Vol. 60; no. 2; pp. 320 - 330
• Main Author: Fainzilberg, L. S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Engineering; Computing time; Control; Decision analysis; Electrocardiogram; Electrocardiography; Informatics; Mathematics; Mathematics and Statistics; New Means of Cybernetics; Processor Architectures; Sequential analysis; Software Engineering/Programming and Operating Systems; Stochastic models; Systems Analysis; Systems Theory; statistical experiment; computer algorithm efficiency; artificial data generating
• ISSN: 1060-0396; 1573-8337
• DOI: 10.1007/s10559-024-00672-9

The author formulates definitions of computer algorithm efficiency according to a criterion that characterizes accuracy, reliability, performance speed, and other consumer properties. Schemes of proof experiments based on stochastic models of generating artificial data with statistical characteristics adequate to real observations are suggested. The experiments are aimed at determining the efficiency of computer algorithms that provide solutions to three different problems, namely, the optimal stopping for making a final decision during a sequential analysis of alternatives, training a linear classifier based on a finite sample of observations, and determining diagnostic signs of an ECG using the fasegraphy method. The results obtained based on statistical experiments are given.