Upper Bounds and Algorithms for Construction of Deterministic Decision Trees for Decision Tables. Second Approach

Mikhail Moshkov

doi:10.1007/978-3-030-41728-4_5

Upper Bounds and Algorithms for Construction of Deterministic Decision Trees for Decision Tables. Second Approach

Mikhail Moshkov^*

^*Corresponding author for this work

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

Abstract

In this chapter, upper bounds on the minimum complexity and algorithms for construction of deterministic decision trees for decision tables are considered. These bounds and algorithms are based on the use of so-called additive-bounded uncertainty measures for decision tables. The bounds are true for any complexity function having the property When developing algorithms, we assume that the complexity functions have properties.

Original language	English (US)
Title of host publication	Intelligent Systems Reference Library
Publisher	Springer
Pages	65-84
Number of pages	20
DOIs	https://doi.org/10.1007/978-3-030-41728-4_5
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

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10.1007/978-3-030-41728-4_5

Cite this

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Upper Bounds and Algorithms for Construction of Deterministic Decision Trees for Decision Tables. Second Approach

Abstract

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