INTERESSABILIDADE DE MODELOS DE REGRESSÃO EM MINERAÇÃO DE DADOS AGRÍCOLAS

Detalhes bibliográficos
Ano de defesa: 2015
Autor(a) principal: Estevam Junior, Valter Luís lattes
Orientador(a): Guimarães, Alaine Margarete lattes
Banca de defesa: Pozo, Aurora Trinidad Ramirez lattes, Garbuio, Fernando José lattes
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
Programa de Pós-Graduação: Programa de Pós Graduação Computação Aplicada
Departamento: Computação para Tecnologias em Agricultura
País: BR
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/121
Resumo: The interestingness area of data mining process aiming to reduce the amount of models to be analyzed for experts in the interpretation step of the knowledge discovery in databases. In this work, a method for analysis the interestingness of regression models was developed. This method combine probabilistic multivariate models with Pearson correlation test and Wilcoxon signed-rank test resulting in a new interestingness measure, named Impact. The developed method was applied over regression models found during a data mining process for estimating agricultural gypsum requirements. The results showed that the probabilistic multivariate filter was able to filter the best models according to a utility-based approach, in this case, for practical application on agriculture. Six models were considered interesting, with Impact score > 0.5, and only one was miscategorized. On the other hand, the combined statistical test filters were able to filter six models two of them were miscategorized. The attributes identified as most relevant to estimate gypsum rate were: time, Ca and its concentration on effective cation exchange capacity (CaCTCe), mainly in superficial layers.