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A feature selection approach in the study of azorean proverbs

dc.contributor.authorCavique, Luís
dc.contributor.authorMendes, Armando B.
dc.contributor.authorFunk, Matthias
dc.contributor.authorSantos, Jorge M. A.
dc.date.accessioned2014-01-13T16:38:11Z
dc.date.available2014-01-13T16:38:11Z
dc.date.issued2013-11
dc.description.abstractA paremiologic (study of proverbs) case is presented as part of a wider project based on data collected among the Azorean population. Given the considerable distance between the Azores islands, we present the hypothesis that there are significant differences in the proverbs from each island, thus permitting the identification of the native island of the interviewee, based on his or her knowledge of proverbs. In this chapter, a feature selection algorithm that combines Rough Sets and the Logical Analysis of Data (LAD) is presented. The algorithm named LAID (Logical Analysis of Inconsistent Data) deals with noisy data, and we believe that an important link was established between the two different schools with similar approaches. The algorithm was applied to a real world dataset based on data collected using thousands of interviews of Azoreans, involving an initial set of twenty-two thousand Portuguese proverbs.por
dc.identifierDOI: 10.4018/978-1-4666-4785-5.ch003
dc.identifier.isbn146664785X
dc.identifier.isbn9781466447855
dc.identifier.urihttp://hdl.handle.net/10400.2/2795
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIGI Globalpor
dc.subjectData miningpor
dc.subjectFeature selectionpor
dc.subjectLogicla analysis of datapor
dc.subjectRough setspor
dc.titleA feature selection approach in the study of azorean proverbspor
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage58por
oaire.citation.startPage38por
oaire.citation.titleExploring Innovative and Successful Applications of Soft Computingpor
person.familyNameCavique
person.familyNameB Mendes
person.familyNameSantos
person.givenNameLuís
person.givenNameArmando
person.givenNameJorge
person.identifier1008054
person.identifier.ciencia-id911E-84AC-3956
person.identifier.ciencia-idEE1E-90E7-2751
person.identifier.ciencia-id8913-96E9-2FEE
person.identifier.orcid0000-0002-5590-1493
person.identifier.orcid0000-0003-3049-5852
person.identifier.orcid0000-0003-1000-8252
person.identifier.ridN-7280-2015
person.identifier.ridN-7809-2013
person.identifier.scopus-author-id13003839500
person.identifier.scopus-author-id55462369200
rcaap.rightsopenAccesspor
rcaap.typebookPartpor
relation.isAuthorOfPublication40906a16-46a2-42f1-b26d-7db7012294ee
relation.isAuthorOfPublicationd26eb57f-648e-485c-bd92-efcc8cb1b3be
relation.isAuthorOfPublication80af4f84-54a0-4647-a85a-58e8c0f81634
relation.isAuthorOfPublication.latestForDiscovery40906a16-46a2-42f1-b26d-7db7012294ee

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