Data Mining e Análise Inferencial para Grandes Bancos de Dados
Data Mining em Medicina
Nesta palestra serão consideradas abordagens estatísticas em Medicina: baseada em modelos e a algorítmica. A visão de Sackett da estatística baseada em modelos para o clínico.
Exemplos da abordagem algorítmica de Breiman: árvores de classificação, árvores de sobrevida, redes neurais feedfoward, redes neurais probabilísticas, redes de Kohonen, support vector machine, modelos log-lineares e grafos, e visualização de dados.
Data Mining And Statistical Perspectives In HDLSS Models
The ongoing evolution of genomics and bioinformatics has generated massive datasets in enormously large dimensions and in incredible pace. Due to excessive cost of data collection, the sample size is generally disproportionately small, thus leading to the so called high-dimension low sample size (HDLSS) models (K > > n).
Bagging K-Dependence Bayesian Classifiers for Classification Modeling On Large Datasets
In large datasets, classification modeling comprises one of the leading formal tools for supporting the decision making. For instance, in industrial studies a component should be detected for defect. In biomedical studies it is important to determining if a patient is committed with a disease. In financial studies the core objective consists on the generation of a score by means of which potential clients can be listed in order of the probability of default.
