Informatics and Applications
2020, Volume 14, Issue 1, pp 80-86
ON CAUSAL REPRESENTATIVENESS OF TRAINING SAMPLES OF PRECEDENTS IN DIAGNOSTIC TYPE TASKS
- A. A. Grusho
- M. I. Zabezhailo
- E. E. Timonina
Abstract
The work focuses on some features of causality analysis in data mining tasks. The possibilities of using so-called open logic theories in diagnostic (classification) tasks to describe replenished sets of empirical data are discussed. In tasks of this type, it is necessary to establish (predict, diagnose, etc.) the presence or absence of a target property in a new precedent given by a description in the same presentation language of heterogeneous data, which describes examples having a target property and counter-examples not having a target property. The variant of construction of open theories describing collections of precedents by means of special logical expressions - characteristic functions - is presented. Characteristic functions allow to get rid of heterogeneity in descriptions of precedents. The procedural design of formation of characteristic functions of a training sample of precedents is proposed. The properties of characteristic functions and some conditions of their existence are studied.
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[+] About this article
Title
ON CAUSAL REPRESENTATIVENESS OF TRAINING SAMPLES OF PRECEDENTS IN DIAGNOSTIC TYPE TASKS
Journal
Informatics and Applications
2020, Volume 14, Issue 1, pp 80-86
Cover Date
2020-03-30
DOI
10.14357/19922264200111
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
diagnostics; causal analysis; intelligent data analysis; open logic
Authors
A. A. Grusho  , M. I. Zabezhailo  , and E. E. Timonina
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
Dorodnicyn Computing Center, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 40 Vavilov Str., Moscow 119333, Russian Federation
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