A carregar...
Publicação
Classification and combining models
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 79.5 KB | Adobe PDF |
Orientador(es)
Resumo(s)
In the context of Discrete Discriminant Analysis (DDA) the idea of combining models is present in a growing number of papers aiming to obtain more robust and more stable models. This seems to be a promising approach since it is known that different DDA models perform differently on different subjects. Furthermore, the idea of combining models is particularly relevant when the groups are not well separeted, which often occurs in practice. Recently, we proposed a new DDA approach which is based on a linear combination of the First-order Independence Model (FOIM) and the Dependence Trees Model (DTM). In the present work we apply this new approach to classify consumers of a Portuguese cultural institution. We specifically focus on the performance of alternative models' combinations assessing the error rate and the Huberty index in a test sample. We use the R software for the algorithms' implementation and evaluation.
Descrição
Trabalho apresentado em SMTDA 2010: Stochastic Modeling Techniques and Data Analysis International Conference, Chania, Crete, Greece, 8-11 june 2010
Palavras-chave
Combining models Dependence Trees Models Discrete Discriminant Analysis First-order Independence Model
Contexto Educativo
Citação
In SMTDA2010 Proceedings