Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications

Fawaz Alsolami; Igor Chikalov; Mikhail Moshkov

doi:10.1007/978-3-642-41299-8_15

Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications

Fawaz Alsolami, Igor Chikalov, Mikhail Moshkov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. © 2013 Springer-Verlag.

Original language	English (US)
Title of host publication	Rough Sets and Knowledge Technology
Publisher	Springer Nature
Pages	154-165
Number of pages	12
ISBN (Print)	9783642412981
DOIs	https://doi.org/10.1007/978-3-642-41299-8_15
State	Published - 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-41299-8_15

Cite this

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title = "Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications",

abstract = "This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. {\textcopyright} 2013 Springer-Verlag.",

author = "Fawaz Alsolami and Igor Chikalov and Mikhail Moshkov",

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AU - Alsolami, Fawaz

AU - Chikalov, Igor

AU - Moshkov, Mikhail

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2013

Y1 - 2013

N2 - This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. © 2013 Springer-Verlag.

AB - This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. © 2013 Springer-Verlag.

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UR - http://www.scopus.com/inward/record.url?scp=84887430088&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-41299-8_15

DO - 10.1007/978-3-642-41299-8_15

M3 - Conference contribution

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EP - 165

BT - Rough Sets and Knowledge Technology

PB - Springer Nature

ER -

Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications

Abstract

Bibliographical note

ASJC Scopus subject areas

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