Using Multiple Instance Learning techniques to rank maize ears according to their traits
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/105451 |
Resumo: | Abstract Multiple-Instance Learning (MIL) is a sub-field of machine learning. Its main goal is to do accurate predictions on new data based on a predictive model generated from previously group of labeled bags of data, known as training data, containing many instances. MIL has many real world important applications such as image retrieval or text categorization and medical diagnosis problems. It is often difficult for crop breeders to predict yield by combining different yield components to produce better plants with superior performance. Data analysis is one area that is striving to let farmers have an idea of their expected yield pre-harvest. Accurate early yield prediction will improve agricultural strategies plan, proper resources allocation and improve management of maize ear cultivation with consequent increase in productivity. Most experiments on maize ears traits only considered ear evaluation and maize improvement without yield prediction. One of the experiments that included yield prediction was PR. NDCG measure which was developed to rank maize evaluation for Sousa Valley Best Ear Competition. The focus of this work was to make an intelligent regression models recognition and analysis by running some MIL algorithms to predict and assign real value to maize yield from randomly group N vary parameter sizes of maize ear traits and soil parameters of maize population dataset. Furthermore, this dissertation also ranked the models per result and establish a relationship between variables. |
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Using Multiple Instance Learning techniques to rank maize ears according to their traitsEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringAbstract Multiple-Instance Learning (MIL) is a sub-field of machine learning. Its main goal is to do accurate predictions on new data based on a predictive model generated from previously group of labeled bags of data, known as training data, containing many instances. MIL has many real world important applications such as image retrieval or text categorization and medical diagnosis problems. It is often difficult for crop breeders to predict yield by combining different yield components to produce better plants with superior performance. Data analysis is one area that is striving to let farmers have an idea of their expected yield pre-harvest. Accurate early yield prediction will improve agricultural strategies plan, proper resources allocation and improve management of maize ear cultivation with consequent increase in productivity. Most experiments on maize ears traits only considered ear evaluation and maize improvement without yield prediction. One of the experiments that included yield prediction was PR. NDCG measure which was developed to rank maize evaluation for Sousa Valley Best Ear Competition. The focus of this work was to make an intelligent regression models recognition and analysis by running some MIL algorithms to predict and assign real value to maize yield from randomly group N vary parameter sizes of maize ear traits and soil parameters of maize population dataset. Furthermore, this dissertation also ranked the models per result and establish a relationship between variables.2017-07-102017-07-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/105451TID:201804700engKaramot Kehinde Biliaminuinfo: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:RCAAP2023-11-29T14:19:13Zoai:repositorio-aberto.up.pt:10216/105451Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:58:49.898301Repositório 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 |
Using Multiple Instance Learning techniques to rank maize ears according to their traits |
title |
Using Multiple Instance Learning techniques to rank maize ears according to their traits |
spellingShingle |
Using Multiple Instance Learning techniques to rank maize ears according to their traits Karamot Kehinde Biliaminu Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Using Multiple Instance Learning techniques to rank maize ears according to their traits |
title_full |
Using Multiple Instance Learning techniques to rank maize ears according to their traits |
title_fullStr |
Using Multiple Instance Learning techniques to rank maize ears according to their traits |
title_full_unstemmed |
Using Multiple Instance Learning techniques to rank maize ears according to their traits |
title_sort |
Using Multiple Instance Learning techniques to rank maize ears according to their traits |
author |
Karamot Kehinde Biliaminu |
author_facet |
Karamot Kehinde Biliaminu |
author_role |
author |
dc.contributor.author.fl_str_mv |
Karamot Kehinde Biliaminu |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
description |
Abstract Multiple-Instance Learning (MIL) is a sub-field of machine learning. Its main goal is to do accurate predictions on new data based on a predictive model generated from previously group of labeled bags of data, known as training data, containing many instances. MIL has many real world important applications such as image retrieval or text categorization and medical diagnosis problems. It is often difficult for crop breeders to predict yield by combining different yield components to produce better plants with superior performance. Data analysis is one area that is striving to let farmers have an idea of their expected yield pre-harvest. Accurate early yield prediction will improve agricultural strategies plan, proper resources allocation and improve management of maize ear cultivation with consequent increase in productivity. Most experiments on maize ears traits only considered ear evaluation and maize improvement without yield prediction. One of the experiments that included yield prediction was PR. NDCG measure which was developed to rank maize evaluation for Sousa Valley Best Ear Competition. The focus of this work was to make an intelligent regression models recognition and analysis by running some MIL algorithms to predict and assign real value to maize yield from randomly group N vary parameter sizes of maize ear traits and soil parameters of maize population dataset. Furthermore, this dissertation also ranked the models per result and establish a relationship between variables. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-07-10 2017-07-10T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/105451 TID:201804700 |
url |
https://hdl.handle.net/10216/105451 |
identifier_str_mv |
TID:201804700 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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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1799135911052574720 |