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1 paper
Classification problem in conditions of distorted cause-and-effect relationships
A. A. Grusho,
N. A. Grusho,
M. I. Zabezhailo,
A. A. Zatsarinny,
E. E. Timonina,
S. Ya. Shorgin Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119133, Russian Federation
Abstract:
The paper considers the model for classifying an object stream for the presence or absence of a certain property
$A$ in each object
$O$. It is assumed that there are
$M$ bijective transformations of objects coming for classification and in the stream, there are objects obtained from
$O$ according to one of these transformations. For each object
$O$, it is known that it contains property
$A$ which causes the known objects
$B_1, B_2, \ldots , B_k$ to appear in information spaces
$I_1, I_2, \ldots , I_k$. This means that property
$A$ can only be detected by observing the consequences
$B_1, B_2, \ldots , B_k$. The problem is that for each object in the flow, it is necessary to determine the presence or absence of the converted reason
$A$ in it. The algorithms for checking such possibility are built in cases where there is a description of characteristics
$A$ and when such a description is absent.
Keywords:
finite classification task, cause-and-effect relationships, machine learning in distortions conditions. Received: 13.02.2023
DOI:
10.14357/08696527230106