Rodyti trumpą aprašą

dc.contributor.authorŠtrimaitis, Rokas
dc.contributor.authorStefanovič, Pavel
dc.contributor.authorRamanauskaitė, Simona
dc.contributor.authorSlotkienė, Asta
dc.date.accessioned2023-09-18T16:17:36Z
dc.date.available2023-09-18T16:17:36Z
dc.date.issued2022
dc.identifier.issn1687-5265
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/112866
dc.description.abstractAutomated data analysis solutions are very dependent on data and its quality. The possibility of assigning more than one class to the same data item is one of the specificities that need to be taken into account. There are no solutions, dedicated to Lithuanian text data classification that helps to assign more than one class to data item. In this paper, a new combined approach has been proposed for multilabel text data classification for text analysis. The main aim of the proposed approach is to improve the accuracy of traditional classification algorithms by incorporating the results obtained using similarity measures. The experimental investigation has been performed using the financial news multilabel text data in the Lithuanian language. Data have been collected from four public websites and classified by experts into ten classes manually, where each of the data items has no more than two classes. The results of five commonly used algorithms have been compared for dataset classification: the support vector machine, multinomial naive Bayes, k-nearest neighbours, decision trees, linear and discriminant analysis. In addition, two similarity measures have been compared: the cosine distance and the dice coefficient. Research has shown that the best results have been obtained using the cosine similarity distance and the multinomial naive Bayes classifier. The proposed approach combines the results of these two methods. Research on different cases of the proposed approach indicated the peculiarities of its application. At the same time, the combined approach allowed us to obtain a statistically significant increase in global accuracy.eng
dc.formatPDF
dc.format.extentp. 1-13
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyINSPEC
dc.relation.isreferencedbyMEDLINE
dc.relation.isreferencedbyProQuest Central
dc.relation.isreferencedbyPubMed
dc.source.urihttps://www.hindawi.com/journals/cin/2022/3369703/
dc.titleA combined approach for multi-label text data classification
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references28
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.vgtuprioritizedfieldsIK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enmulti-label text data
dc.subject.ensimilarity distance
dc.subject.enclassification
dc.subject.enLithuanian language
dc.subject.enfinancial text data
dcterms.sourcetitleComputational intelligence and neuroscience
dc.description.volumevol. 2022
dc.publisher.nameHindawi
dc.publisher.cityLondon
dc.identifier.doi000820934600009
dc.identifier.doi10.1155/2022/3369703
dc.identifier.elaba134438204


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