INTERESSABILIDADE DE MODELOS DE REGRESSÃO EM MINERAÇÃO DE DADOS AGRÍCOLAS

• Autor(a) principal: Estevam Junior, Valter Luís
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UEPG
• Texto Completo: http://tede2.uepg.br/jspui/handle/prefix/121
• topic: gesso agrícola; redes bayesianas; sensitividade; impacto; gypsum; bayesian networks; sensitivity; impact; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

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.