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Export Reference (APA) 
Ferreira, A. & Cardoso, M. G. M. S. (2013). Evaluating discriminant analysis results. In João Lita da Silva, Frederico Caeiro, Isabel Natário, Carlos A. Braumann (Ed.), Advances in regression, survival analysis, extreme values, Markov: Processes and other statistical applications . (pp. 155-162). Berlin: Sringer.
Export Reference (IEEE) 
A. M. Ferreira and M. M. Cardoso,  "Evaluating discriminant analysis results", in Advances in regression, survival analysis, extreme values, Markov: Processes and other statistical applications , João Lita da Silva, Frederico Caeiro, Isabel Natário, Carlos A. Braumann, Ed., Berlin, Sringer, 2013, pp. 155-162
Export BibTeX 
@incollection{ferreira2013_1716224756681,
	author = "Ferreira, A. and Cardoso, M. G. M. S.",
	title = "Evaluating discriminant analysis results",
	chapter = "",
	booktitle = "Advances in regression, survival analysis, extreme values, Markov: Processes and other statistical applications ",
	year = "2013",
	volume = "",
	series = "Studies in Theoretical and Applied Statistics",
	edition = "",
	pages = "155-155",
	publisher = "Sringer",
	address = "Berlin",
	url = "https://link.springer.com/chapter/10.1007/978-3-642-34904-1_16#citeas"
}
Export RIS 
TY  - CHAP
TI  - Evaluating discriminant analysis results
T2  - Advances in regression, survival analysis, extreme values, Markov: Processes and other statistical applications 
AU  - Ferreira, A.
AU  - Cardoso, M. G. M. S.
PY  - 2013
SP  - 155-162
DO  - 10.1007/978-3-642-34904-1_16
CY  - Berlin
UR  - https://link.springer.com/chapter/10.1007/978-3-642-34904-1_16#citeas
AB  - In discrete discriminant analysis (DDA) different models often exhibit different classification performances. Therefore, the idea of combining models has increasingly gained importance. In the present work we focus on the evaluation of alternative DDA models, including combined models. The proposed approach uses not only the classic indicators of classification precision but also indices of agreement that regard the relationship between the actual classes and the ones predicted by discriminant analysis. The performance of the DDA methods is analyzed based on simulated binary data, using small and moderate sample sizes. The results obtained illustrate the potential of combining DDA models, offering different evaluation perspectives.
ER  -