Publicação
Generating and updating supervised Data Mining models on a periodic basis
| Resumo: | Data mining techniques are currently of great importance in companies and organisations worldwide for building predictive models. These models are particularly useful for classifying new data and supporting decision-making processes by helping to make the most appropriate decisions. However, over time, the predictive models created can become outdated as the patterns found in the data change due to natural evolution. This aspect can affect the quality of the models and lead to results that do not match reality. In this paper, we present a general approach for creating a self-updating system of predictive models that can be adapted to specific contexts. This system periodically generates and selects the most appropriate predictive model for ensuring the validity of its predictions. It integrates data processing and data mining model generation, and allows for the detection of changes in existing patterns as new data is added. This is suitable for supervised data mining tasks that may be affected by data evolution. The implementation of the system has demonstrated that it is possible to pre-process the data and select the best predictive model. In addition, since the execution is triggered automatically, the need for system maintenance is reduced. |
|---|---|
| Autores principais: | Duarte, Ana |
| Outros Autores: | Belo, Orlando |
| Assunto: | Concept drift Data mining Pentaho data integration Self-updating models Weka Workflow |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866876678348734464 |
|---|---|
| author | Duarte, Ana |
| author2 | Belo, Orlando |
| author2_role | author |
| author_facet | Duarte, Ana Belo, Orlando |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Duarte, Ana\"},{\"Person.name\":\"Belo, Orlando\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Duarte, Ana Belo, Orlando |
| datacite.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| datacite.date.embargoed.fl_str_mv | 10000-01-01T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Concept drift Data mining Pentaho data integration Self-updating models Weka Workflow |
| datacite.titles.title.fl_str_mv | Generating and updating supervised Data Mining models on a periodic basis |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Duarte, Ana Belo, Orlando |
| dc.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.date.embargoed.fl_str_mv | 10000-01-01T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/90751 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Concept drift Data mining Pentaho data integration Self-updating models Weka Workflow |
| dc.title.fl_str_mv | Generating and updating supervised Data Mining models on a periodic basis |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Data mining techniques are currently of great importance in companies and organisations worldwide for building predictive models. These models are particularly useful for classifying new data and supporting decision-making processes by helping to make the most appropriate decisions. However, over time, the predictive models created can become outdated as the patterns found in the data change due to natural evolution. This aspect can affect the quality of the models and lead to results that do not match reality. In this paper, we present a general approach for creating a self-updating system of predictive models that can be adapted to specific contexts. This system periodically generates and selects the most appropriate predictive model for ensuring the validity of its predictions. It integrates data processing and data mining model generation, and allows for the detection of changes in existing patterns as new data is added. This is suitable for supervised data mining tasks that may be affected by data evolution. The implementation of the system has demonstrated that it is possible to pre-process the data and select the best predictive model. In addition, since the execution is triggered automatically, the need for system maintenance is reduced. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/c791018c-28d8-4ce7-b0e2-12d081f13cd7/download |
| id | rum_fd80f151e306cea8a595fc4ccc4cb494 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/90751 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/90751 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Duarte, Ana Belo, Orlando |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Springer |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engSpringerporData mining techniques are currently of great importance in companies and organisations worldwide for building predictive models. These models are particularly useful for classifying new data and supporting decision-making processes by helping to make the most appropriate decisions. However, over time, the predictive models created can become outdated as the patterns found in the data change due to natural evolution. This aspect can affect the quality of the models and lead to results that do not match reality. In this paper, we present a general approach for creating a self-updating system of predictive models that can be adapted to specific contexts. This system periodically generates and selects the most appropriate predictive model for ensuring the validity of its predictions. It integrates data processing and data mining model generation, and allows for the detection of changes in existing patterns as new data is added. This is suitable for supervised data mining tasks that may be affected by data evolution. The implementation of the system has demonstrated that it is possible to pre-process the data and select the best predictive model. In addition, since the execution is triggered automatically, the need for system maintenance is reduced.application/pdfporGenerating and updating supervised Data Mining models on a periodic basisDuarte, AnaBelo, OrlandoHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISBNIsPartOf978-3-031-47714-0ISSNIsPartOf2367-3370DOIIsPartOf10.1007/978-3-031-47715-7_3120242024-04-05T17:50:33Z10000-01-01T00:00:00Z2024-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/90751http://purl.org/coar/access_right/c_16ecrestricted accessConcept driftData miningPentaho data integrationSelf-updating modelsWekaWorkflow493135 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_f1cfapplication/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/c791018c-28d8-4ce7-b0e2-12d081f13cd7/download |
| spellingShingle | Generating and updating supervised Data Mining models on a periodic basis Duarte, Ana Concept drift Data mining Pentaho data integration Self-updating models Weka Workflow |
| status | SINGLETON |
| subject.fl_str_mv | Concept drift Data mining Pentaho data integration Self-updating models Weka Workflow |
| title | Generating and updating supervised Data Mining models on a periodic basis |
| title_full | Generating and updating supervised Data Mining models on a periodic basis |
| title_fullStr | Generating and updating supervised Data Mining models on a periodic basis |
| title_full_unstemmed | Generating and updating supervised Data Mining models on a periodic basis |
| title_short | Generating and updating supervised Data Mining models on a periodic basis |
| title_sort | Generating and updating supervised Data Mining models on a periodic basis |
| topic | Concept drift Data mining Pentaho data integration Self-updating models Weka Workflow |
| topic_facet | Concept drift Data mining Pentaho data integration Self-updating models Weka Workflow |
| url | https://hdl.handle.net/1822/90751 |
| visible | 1 |