Optimization of β-decision rules relative to number of misclassifications

Beata Zielosko

doi:10.1007/978-3-642-34707-8_35

Optimization of β-decision rules relative to number of misclassifications

Beata Zielosko

Computer, Electrical and Mathematical Sciences and Engineering

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

Abstract

In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.

Original language	English (US)
Title of host publication	Computational Collective Intelligence. Technologies and Applications
Publisher	Springer Nature
Pages	345-354
Number of pages	10
ISBN (Print)	9783642347061
DOIs	https://doi.org/10.1007/978-3-642-34707-8_35
State	Published - 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-34707-8_35

Cite this

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AB - In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.

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UR - http://link.springer.com/10.1007/978-3-642-34707-8_35

UR - http://www.scopus.com/inward/record.url?scp=84870953466&partnerID=8YFLogxK

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DO - 10.1007/978-3-642-34707-8_35

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BT - Computational Collective Intelligence. Technologies and Applications

PB - Springer Nature

ER -

Optimization of β-decision rules relative to number of misclassifications

Abstract

Bibliographical note

ASJC Scopus subject areas

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