Discrete discriminant analysis: The performance of combining models

Marques, Anabela; Ferreira, Ana Sousa; Cardoso, Margarida

http://hdl.handle.net/10400.26/8129

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
Resumo_IFCS2009.pdf		37.62 KB	Adobe PDF	Download

Send Feedback

Authors

Marques, Anabela

Ferreira, Ana Sousa

Cardoso, Margarida

Abstract(s)

The idea of combinig models in Discrete Discriminant Analysis (DDA) is present in a growing number of papers which aim to obtain more robust and more stable models than any of the competing ones. This seems to be a promising approach since it is known that different DDA models perform differently on different subjects [1]. This is a particularly relevant issue when the groups are not well separeted, which often occurs in practice. recently, a new methodological approach was proposed based on a linear combination of the First-order Independence Model (FOIM) and the Dependence trees Model (DTM) ([3] and [2]). In the present work we further explore the referred approach. Since FOIM assumes that the P discrete predictive variables are independent in each group and DTM takes the predictors relationships into account, we think that the proposed approach could be sucessfully applied to many real situations. In order to evaluate its performance, we consider both real and simulated data. Furthermore we present comparisons with alternative models performance. According to the training sample size the leave-one-out approach, v-fold cross validation or assessing the error rate in a test sample are considered. The MATLAB software is used for the algorithms' implementation.

Description

Resumo de comunicação oral apresentada em 11th Conference of the International Federation of Classification Societes (IFCS 2009), Dresden, Germany, 13-18 March 2009