Main Reductions

Mikhail Moshkov

doi:10.1007/978-3-030-41728-4_10

Main Reductions

Mikhail Moshkov^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this chapter, we consider some reductions which will be used later in the investigations of decision trees for problems. We show that, in the frameworks of the local approach, the study of decision trees for problems can be reduced to the study of decision trees for decision tables. We prove that, instead of arbitrary classes of information systems, we can consider classes containing only one information system. We also show that the matrix of upper local bounds for a sccf-triple completely defines its matrix of lower local bounds and vice versa. In particular, the local upper type of a sccf-triple completely defines its local lower type and vice versa.

Original language	English (US)
Title of host publication	Intelligent Systems Reference Library
Publisher	Springer
Pages	125-135
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-030-41728-4_10
State	Published - 2020

Publication series

Name	Intelligent Systems Reference Library
Volume	179
ISSN (Print)	1868-4394
ISSN (Electronic)	1868-4408

Bibliographical note

Publisher Copyright:
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus subject areas

General Computer Science
Information Systems and Management
Library and Information Sciences

Access to Document

10.1007/978-3-030-41728-4_10

Cite this

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abstract = "In this chapter, we consider some reductions which will be used later in the investigations of decision trees for problems. We show that, in the frameworks of the local approach, the study of decision trees for problems can be reduced to the study of decision trees for decision tables. We prove that, instead of arbitrary classes of information systems, we can consider classes containing only one information system. We also show that the matrix of upper local bounds for a sccf-triple completely defines its matrix of lower local bounds and vice versa. In particular, the local upper type of a sccf-triple completely defines its local lower type and vice versa.",

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AB - In this chapter, we consider some reductions which will be used later in the investigations of decision trees for problems. We show that, in the frameworks of the local approach, the study of decision trees for problems can be reduced to the study of decision trees for decision tables. We prove that, instead of arbitrary classes of information systems, we can consider classes containing only one information system. We also show that the matrix of upper local bounds for a sccf-triple completely defines its matrix of lower local bounds and vice versa. In particular, the local upper type of a sccf-triple completely defines its local lower type and vice versa.

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ER -

Main Reductions

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ASJC Scopus subject areas

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