Publicação
Features selection in Discrete Discriminant Analysis
| Resumo: | In discrete discriminant analysis dimensionality problems occur, particularly when dealing with data from the social sciences, humanities and health. In these domains, one often has to classify entities with a high number of explanatory variables when compared to the number of observations available. In the present work we address the problem of features selection in classification, aiming to identify the variables that most discriminate between the a priori defined classes, reducing the number of parameters to estimate, turning the results easier to interpret and reducing the runtime of the methods used. We specially address classification using a recently methodological approach based on a linear combination of the First-order Independence Model (FOIM) and the Dependence Trees Model (DTM). Data of small and moderate size are considered. |
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| Autores principais: | Marques, Anabela |
| Outros Autores: | Ferreira, Ana Sousa; Cardoso, Margarida |
| Assunto: | Discrete Discriminant Analysis Combining models Dependence Trees model First Order Independence model Hierarchical Coupling procedure Variable selection |
| Ano: | 2011 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Setúbal |
| Idioma: | inglês |
| Origem: | Instituto Politécnico de Setúbal |
| Resumo: | In discrete discriminant analysis dimensionality problems occur, particularly when dealing with data from the social sciences, humanities and health. In these domains, one often has to classify entities with a high number of explanatory variables when compared to the number of observations available. In the present work we address the problem of features selection in classification, aiming to identify the variables that most discriminate between the a priori defined classes, reducing the number of parameters to estimate, turning the results easier to interpret and reducing the runtime of the methods used. We specially address classification using a recently methodological approach based on a linear combination of the First-order Independence Model (FOIM) and the Dependence Trees Model (DTM). Data of small and moderate size are considered. |
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