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Marques, A., Ferreira, A. S. & Cardoso, M. G. M. S. (2016). Combining models in discrete discriminant analysis. International Journal of Data Analysis Techniques and Strategies. 8 (2), 143-160
Export Reference (IEEE) 
A. Marques et al.,  "Combining models in discrete discriminant analysis", in Int. Journal of Data Analysis Techniques and Strategies, vol. 8, no. 2, pp. 143-160, 2016
Export BibTeX 
@article{marques2016_1732203625822,
	author = "Marques, A. and Ferreira, A. S. and Cardoso, M. G. M. S.",
	title = "Combining models in discrete discriminant analysis",
	journal = "International Journal of Data Analysis Techniques and Strategies",
	year = "2016",
	volume = "8",
	number = "2",
	doi = "10.1504/IJDATS.2016.077483",
	pages = "143-160",
	url = "http://www.inderscience.com/offer.php?id=77483"
}
Export RIS 
TY  - JOUR
TI  - Combining models in discrete discriminant analysis
T2  - International Journal of Data Analysis Techniques and Strategies
VL  - 8
IS  - 2
AU  - Marques, A.
AU  - Ferreira, A. S.
AU  - Cardoso, M. G. M. S.
PY  - 2016
SP  - 143-160
SN  - 1755-8050
DO  - 10.1504/IJDATS.2016.077483
UR  - http://www.inderscience.com/offer.php?id=77483
AB  - 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.
ER  -