Combining models in discrete discriminant analysis

When conducting discrete discriminant analysis, alternative models provide different levels of predictive accuracy which has encouraged the research in combined models. This research seems to be specially promising when small or moderate sized samples are considered, which often occurs in practice. In this work we evaluate the performance of a linear combination of two discrete discriminant analysis models: the first-order independence model and the dependence trees model. The proposed methodology also uses a hierarchical coupling model when addressing multi-class classification problems, decomposing the multi-class problems into several bi-class problems, using a binary tree structure. The analysis is based both on simulated and real datasets. Results of the proposed approach are compared with those obtained by random forests, being generally more accurate. Measures of precision regarding a training set, a test set and cross-validation are presented. The R software is used for the algorithms' implementation.discrete discriminant analysis; DDA; combining models; dependence trees model; DTM; first-order independence model; FOIM; hierarchical coupling model; HIERM; random forest; modelling