DI2
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/128950 |
Resumo: | CEECIND/01399/2017 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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DI2prior-free and multi-item discretization of biological data and its applicationsData miningHeterogeneous biological dataMulti-item discretizationPrior-free discretizationStructural BiologyBiochemistryMolecular BiologyComputer Science ApplicationsApplied MathematicsCEECIND/01399/2017Background: A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. Results: In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. Conclusions: This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2LAQV@REQUIMTEDQ - Departamento de QuímicaRUNAlexandre, LeonardoCosta, Rafael S.Henriques, Rui2021-12-09T23:39:53Z2021-122021-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/128950eng1471-2105PURE: 34773354https://doi.org/10.1186/s12859-021-04329-8info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:08:15Zoai:run.unl.pt:10362/128950Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:25.817933Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
DI2 prior-free and multi-item discretization of biological data and its applications |
title |
DI2 |
spellingShingle |
DI2 Alexandre, Leonardo Data mining Heterogeneous biological data Multi-item discretization Prior-free discretization Structural Biology Biochemistry Molecular Biology Computer Science Applications Applied Mathematics |
title_short |
DI2 |
title_full |
DI2 |
title_fullStr |
DI2 |
title_full_unstemmed |
DI2 |
title_sort |
DI2 |
author |
Alexandre, Leonardo |
author_facet |
Alexandre, Leonardo Costa, Rafael S. Henriques, Rui |
author_role |
author |
author2 |
Costa, Rafael S. Henriques, Rui |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
LAQV@REQUIMTE DQ - Departamento de Química RUN |
dc.contributor.author.fl_str_mv |
Alexandre, Leonardo Costa, Rafael S. Henriques, Rui |
dc.subject.por.fl_str_mv |
Data mining Heterogeneous biological data Multi-item discretization Prior-free discretization Structural Biology Biochemistry Molecular Biology Computer Science Applications Applied Mathematics |
topic |
Data mining Heterogeneous biological data Multi-item discretization Prior-free discretization Structural Biology Biochemistry Molecular Biology Computer Science Applications Applied Mathematics |
description |
CEECIND/01399/2017 |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-09T23:39:53Z 2021-12 2021-12-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/128950 |
url |
http://hdl.handle.net/10362/128950 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1471-2105 PURE: 34773354 https://doi.org/10.1186/s12859-021-04329-8 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138067781517312 |