Artigo em revista científica Q3
Combining models in discrete discriminant analysis
Anabela Marques (Marques, A.); Ana Sousa Ferreira (Ferreira, A. S.); Margarida G. M. S. Cardoso (Cardoso, M. G. M. S.);
Título Revista
International Journal of Data Analysis Techniques and Strategies
Ano
2016
Língua
Inglês
País
Suíça
Mais Informação
Scopus

N.º de citações: 0

(Última verificação: 2019-08-18 20:42)

Ver o registo na Scopus

Abstract/Resumo
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.
Agradecimentos/Acknowledgements
--
Palavras-chave
Combining models,DDA,Dependence trees model,Discrete discriminant analysis,DTM,First-order independence model,FOIM,Hierarchical coupling model,HIERM,Random forest,RF
  • Matemáticas - Ciências Naturais
  • Ciências da Computação e da Informação - Ciências Naturais
Registos de financiamentos
Referência de financiamento Entidade Financiadora
UID/GES/00315/2013 Fundação para a Ciência e a Tecnologia