Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/12288
Author(s): | Marques, A. Ferreira, A. S. Cardoso, M. G. M. S. |
Date: | 2016 |
Title: | Combining models in discrete discriminant analysis |
Volume: | 8 |
Number: | 2 |
Pages: | 143 - 160 |
ISSN: | 1755-8050 |
DOI (Digital Object Identifier): | 10.1504/IJDATS.2016.077483 |
Keywords: | Combining models DDA Dependence trees model Discrete discriminant analysis DTM First-order independence model FOIM Hierarchical coupling model HIERM Random forest RF |
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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
File | Description | Size | Format | |
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Publicação_IJDAT_2016_VersãoInicial.pdf | Pré-print | 271,87 kB | Adobe PDF | View/Open |
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