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Article: An interval set model for learning rules from incomplete information table

TitleAn interval set model for learning rules from incomplete information table
Authors
KeywordsIncomplete Information Table
Interval Extension
Interval Set
Rule Induction
Issue Date2012
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ijar
Citation
International Journal Of Approximate Reasoning, 2012, v. 53 n. 1, p. 24-37 How to Cite?
AbstractA novel interval set approach is proposed in this paper to induce classification rules from incomplete information table, in which an interval-set-based model to represent the uncertain concepts is presented. The extensions of the concepts in incomplete information table are represented by interval sets, which regulate the upper and lower bounds of the uncertain concepts. Interval set operations are discussed, and the connectives of concepts are represented by the operations on interval sets. Certain inclusion, possible inclusion, and weak inclusion relations between interval sets are presented, which are introduced to induce strong rules and weak rules from incomplete information table. The related properties of the inclusion relations are proved. It is concluded that the strong rules are always true whatever the missing values may be, while the weak rules may be true when missing values are replaced by some certain known values. Moreover, a confidence function is defined to evaluate the weak rule. The proposed approach presents a new view on rule induction from incomplete data based on interval set. © 2011 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/175504
ISSN
2015 Impact Factor: 2.696
2015 SCImago Journal Rankings: 2.304
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Hen_US
dc.contributor.authorWang, Men_US
dc.contributor.authorZhou, Xen_US
dc.contributor.authorZhao, Jen_US
dc.date.accessioned2012-11-26T08:58:59Z-
dc.date.available2012-11-26T08:58:59Z-
dc.date.issued2012en_US
dc.identifier.citationInternational Journal Of Approximate Reasoning, 2012, v. 53 n. 1, p. 24-37en_US
dc.identifier.issn0888-613Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/175504-
dc.description.abstractA novel interval set approach is proposed in this paper to induce classification rules from incomplete information table, in which an interval-set-based model to represent the uncertain concepts is presented. The extensions of the concepts in incomplete information table are represented by interval sets, which regulate the upper and lower bounds of the uncertain concepts. Interval set operations are discussed, and the connectives of concepts are represented by the operations on interval sets. Certain inclusion, possible inclusion, and weak inclusion relations between interval sets are presented, which are introduced to induce strong rules and weak rules from incomplete information table. The related properties of the inclusion relations are proved. It is concluded that the strong rules are always true whatever the missing values may be, while the weak rules may be true when missing values are replaced by some certain known values. Moreover, a confidence function is defined to evaluate the weak rule. The proposed approach presents a new view on rule induction from incomplete data based on interval set. © 2011 Elsevier Inc. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ijaren_US
dc.relation.ispartofInternational Journal of Approximate Reasoningen_US
dc.subjectIncomplete Information Tableen_US
dc.subjectInterval Extensionen_US
dc.subjectInterval Seten_US
dc.subjectRule Inductionen_US
dc.titleAn interval set model for learning rules from incomplete information tableen_US
dc.typeArticleen_US
dc.identifier.emailWang, M: magwang@hku.hken_US
dc.identifier.authorityWang, M=rp00967en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.ijar.2011.09.002en_US
dc.identifier.scopuseid_2-s2.0-81155139533en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-81155139533&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume53en_US
dc.identifier.issue1en_US
dc.identifier.spage24en_US
dc.identifier.epage37en_US
dc.identifier.isiWOS:000298121800002-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLi, H=24338384700en_US
dc.identifier.scopusauthoridWang, M=8723779700en_US
dc.identifier.scopusauthoridZhou, X=35147441400en_US
dc.identifier.scopusauthoridZhao, J=7410310532en_US
dc.identifier.citeulike9809373-

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