Decision rule classifiers for multi-label decision tables

Fawaz Alsolami; Mohammad Azad; Igor Chikalov; Mikhail Moshkov

doi:10.1007/978-3-319-08729-0_18

Decision rule classifiers for multi-label decision tables

Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov

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

1 Scopus citations

Abstract

Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.

Original language	English (US)
Title of host publication	Rough Sets and Intelligent Systems Paradigms
Publisher	Springer Nature
Pages	191-197
Number of pages	7
ISBN (Print)	9783319087283
DOIs	https://doi.org/10.1007/978-3-319-08729-0_18
State	Published - 2014

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-319-08729-0_18

Cite this

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title = "Decision rule classifiers for multi-label decision tables",

abstract = "Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. {\textcopyright} 2014 Springer International Publishing.",

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AU - Chikalov, Igor

AU - Moshkov, Mikhail

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PY - 2014

Y1 - 2014

N2 - Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.

AB - Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.

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UR - http://link.springer.com/10.1007/978-3-319-08729-0_18

UR - http://www.scopus.com/inward/record.url?scp=84904812253&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-08729-0_18

DO - 10.1007/978-3-319-08729-0_18

M3 - Conference contribution

SN - 9783319087283

SP - 191

EP - 197

BT - Rough Sets and Intelligent Systems Paradigms

PB - Springer Nature

ER -

Decision rule classifiers for multi-label decision tables

Abstract

ASJC Scopus subject areas

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