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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/01/2019 |
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Data da última atualização: |
14/08/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
JOÃO, R. S.; MPINDA, S. T. A.; VIEIRA, A. P. B.; JOÃO, R. S.; ROMANI, L. A. S.; RIBEIRO, M. X. |
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Afiliação: |
RAFAEL S. JOÃO, UFSCar; STEVE T. A. MPINDA, UFSCar; ANA P. B. VIEIRA, UFSCar; RENATO S. JOÃO, UFSCar; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; MARCELA X. RIBEIRO, UFSCar. |
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Título: |
A New approach to classify sugarcane fields based on association rules. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, 14., 2018. Information technology - new generations: proceedings. New York: Springer Science; Business Media, 2018. |
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Páginas: |
p. 475-483 |
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Série: |
(Advances in intelligent systems and computing, 558). |
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DOI: |
10.1007/978-3-319-54978-1_61 |
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Idioma: |
Inglês |
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Conteúdo: |
In order to corroborate the acquired knowledge of the human expert with the use of computational systems in the context of agrocomputing, this work presents a novel classification method for mining agrometeorological remote sensing data and its imple-mentation to identify sugarcane fields, by analyzing Normalized Difference Vegetation Index (NDVI) series. The proposed method, called RAMiner (Rule-based Associative classifier Miner) creates a learning model from sets of mined association rules and employs the rules to constructs an associative classifier. RAMiner was proposed to deal with low spatial resolution image datasets, provided by two sensors/satellites (AVHRR/NOAA and MODIS/Terra). The proposal employs a two-ways classification step for the new data: Considers the conviction value and the conviction-based probability (a weighted accuracy formulated in this work). The results given were compared with others delivered by well-known classifiers, such as C4.5, zeroR, OneR, Naive Bayes, Random Forest and Support Vector Machine (SVM). RAMiner presented the highest accuracy (83.4%),attesting it is well-suited to mine remote sensing data. |
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Palavras-Chave: |
Agrometeorological; Association rules; Associative classification; Data mining; Mineração de dados; Regras de associação. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02144nam a2200277 a 4500
001 2103120
005 2023-08-14
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1007/978-3-319-54978-1_61$2DOI
100 1 $aJOÃO, R. S.
245 $aA New approach to classify sugarcane fields based on association rules.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, 14., 2018. Information technology - new generations: proceedings. New York: Springer Science; Business Media$c2018
300 $ap. 475-483
490 $a(Advances in intelligent systems and computing, 558).
520 $aIn order to corroborate the acquired knowledge of the human expert with the use of computational systems in the context of agrocomputing, this work presents a novel classification method for mining agrometeorological remote sensing data and its imple-mentation to identify sugarcane fields, by analyzing Normalized Difference Vegetation Index (NDVI) series. The proposed method, called RAMiner (Rule-based Associative classifier Miner) creates a learning model from sets of mined association rules and employs the rules to constructs an associative classifier. RAMiner was proposed to deal with low spatial resolution image datasets, provided by two sensors/satellites (AVHRR/NOAA and MODIS/Terra). The proposal employs a two-ways classification step for the new data: Considers the conviction value and the conviction-based probability (a weighted accuracy formulated in this work). The results given were compared with others delivered by well-known classifiers, such as C4.5, zeroR, OneR, Naive Bayes, Random Forest and Support Vector Machine (SVM). RAMiner presented the highest accuracy (83.4%),attesting it is well-suited to mine remote sensing data.
653 $aAgrometeorological
653 $aAssociation rules
653 $aAssociative classification
653 $aData mining
653 $aMineração de dados
653 $aRegras de associação
700 1 $aMPINDA, S. T. A.
700 1 $aVIEIRA, A. P. B.
700 1 $aJOÃO, R. S.
700 1 $aROMANI, L. A. S.
700 1 $aRIBEIRO, M. X.
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| 1. |  | JOÃO, R. S.; MPINDA, S. T. A.; VIEIRA, A. P. B.; JOÃO, R. S.; ROMANI, L. A. S.; RIBEIRO, M. X. A New approach to classify sugarcane fields based on association rules. In: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, 14., 2018. Information technology - new generations: proceedings. New York: Springer Science; Business Media, 2018. p. 475-483 (Advances in intelligent systems and computing, 558).| Tipo: Artigo em Anais de Congresso |
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