Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/4295
Título: | An experiment with association rules and classification: post-bagging and conviction |
Autor(es): | Jorge, Alípio M. Azevedo, Paulo J. |
Palavras-chave: | Association rules Classification |
Data: | 2005 |
Editora: | Springer Verlag |
Revista: | Lecture Notes in Computer Science |
Citação: | HOFFMANN, Achim ; MOTODA, Hiroshi ; SCHEFFER, Tobias, ed. lit. - “Discovery science : proceedings of the International Conference, 8, Singapore, 2005”. Berlin : Springer, 2005. ISBN 3-540-29230-6. p. 137-149. |
Resumo(s): | In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and X². We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/4295 |
ISBN: | 3-540-29230-6 |
DOI: | 10.1007/11563983_13 |
ISSN: | 0302-9743 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DI/CCTC - Artigos (papers) |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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ds05_final.pdf | 431,48 kB | Adobe PDF | Ver/Abrir |