Systems and Means of Informatics
2020, Volume 30, Issue 1, pp 34-45
BAYESIAN CLASSIFICATION OF SERIAL MULTIVARIATE DATA
Abstract
The analysis of the results of observations of a group of objects during the time when these objects can change any of their significant characteristics is considered. The goal is to describe the changes and identify the factors that determine them. Appropriate methods are known as longitudinal. The article proposes a different approach when a series of multivariable characteristics of an object makes up a single vector of the observed values. By increasing the dimension of the data, it is possible to obtain a single picture of the description of objects and to formalize the construction of a data model. To demonstrate the essence of the approach and illustrate the emerging possibilities of data analysis, the problem of early cancer diagnosis using a prostate-specific antigen biomarker is considered. It was revealed that a multivariable approach to the analysis of a series of analyzes leads to an increase in the accuracy of diagnosis.
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[+] About this article
Title
BAYESIAN CLASSIFICATION OF SERIAL MULTIVARIATE DATA
Journal
Systems and Means of Informatics
Volume 30, Issue 1, pp 34-45
Cover Date
2020-05-30
DOI
10.14357/08696527200103
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
serial data classification; longitudinal analysis; consolidation approach; mixture of probabilistic principal component analyzers
Authors
M. P. Krivenko 
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science
and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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