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DOTS: Drift Oriented Tool System

2015-11Book part Restricted access
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorAntunes, Mário
dc.contributor.authorCosta, Joana
dc.contributor.authorSilva, Catarina
dc.contributor.authorBernardete Ribeiro
dc.date.accessioned2025-10-24T14:44:47Z
dc.date.available2025-10-24T14:44:47Z
dc.date.issued2015-11
dc.descriptionPart of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9492)) Source title Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
dc.descriptionConference name 22nd International Conference on Neural Information Processing, ICONIP 2015
dc.descriptionConference city: Istanbul Conference date: 9 November 2015 - 12 November 2015
dc.description.abstractDrift is a given in most machine learning applications. The idea that models must accommodate for changes, and thus be dynamic, is ubiquitous. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. There are multiple drift patterns types: concepts that appear and disappear suddenly, recurrently, or even gradually or incrementally. Researchers strive to propose and test algorithms and techniques to deal with drift in text classification, but it is difficult to find adequate benchmarks in such dynamic environments. In this paper we present DOTS, Drift Oriented Tool System, a framework that allows for the definition and generation of text-based datasets where drift characteristics can be thoroughly defined, implemented and tested. The usefulness of DOTS is presented using a Twitter stream case study. DOTS is used to define datasets and test the effectiveness of using different document representation in a Twitter scenario. Results show the potential of DOTS in machine learning research.eng
dc.description.sponsorshipFunding sponsor: Federación Española de Enfermedades Raras 107002003.
dc.description.sponsorshipAcknowledgment: iCIS project (CENTRO-07-ST24-FEDER - 107002003).
dc.identifier.citationCosta, J., Silva, C., Antunes, M., Ribeiro, B. (2015). DOTS: Drift Oriented Tool System. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_72
dc.identifier.doi10.1007/978-3-319-26561-2_72
dc.identifier.isbn9783319265605
dc.identifier.isbn9783319265612
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10400.8/14369
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-319-26561-2_72
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofNeural Information Processing
dc.rights.uriN/A
dc.subjectDrift
dc.subjectLearning algorithms
dc.subjectSoftware tool
dc.subjectText classification
dc.titleDOTS: Drift Oriented Tool Systemeng
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage623
oaire.citation.startPage615
oaire.citation.titleNeural Information Processing - 22nd International Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV
oaire.citation.volume9492
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
person.familyNameAntunes
person.familyNameCosta
person.familyNameSilva
person.givenNameMário
person.givenNameJoana
person.givenNameCatarina
person.identifierR-000-NX4
person.identifier.ciencia-idAF10-7EDD-5153
person.identifier.ciencia-id1B19-3DDC-BE75
person.identifier.orcid0000-0003-3448-6726
person.identifier.orcid0000-0002-4053-5718
person.identifier.orcid0000-0002-5656-0061
person.identifier.scopus-author-id25930820200
relation.isAuthorOfPublicatione3e87fb0-d1d6-44c3-985d-920a5560f8c1
relation.isAuthorOfPublication23d200dc-1a81-4bd9-9a4a-0efc28af6ce4
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublication.latestForDiscoverye3e87fb0-d1d6-44c3-985d-920a5560f8c1

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