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http://hdl.handle.net/10071/12288
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Title: Combining models in discrete discriminant analysis
Authors: Marques, A.
Ferreira, A. S.
Cardoso, M. G. M. S.
Keywords: Combining models
DDA
Dependence trees model
Discrete discriminant analysis
DTM
First-order independence model
FOIM
Hierarchical coupling model
HIERM
Random forest
RF
Issue Date: 2016
Publisher: Inderscience
Abstract: 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.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/12288
DOI: 10.1504/IJDATS.2016.077483
ISSN: 1755-8050
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-29254
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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