2005
Autores
Ferreira, PG; Alves, R; Azevedo, PJ; Belo, O;
Publicação
Actas de las X Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2005), September 14-16, 2005, Granada, Spain
Abstract
2005
Autores
Camacho, R; Alves, A; da Costa, JP; Azevedo, P;
Publicação
2005 Portuguese Conference on Artificial Intelligence, Proceedings
Abstract
2005
Autores
Camacho, R; Alves, A; Da Costa, JP; Azevedo, P;
Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
2005
Autores
Camacho, R; Alves, A; da Costa, JP; Azevedo, P;
Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS
Abstract
2005
Autores
Jorge, AM; Azevedo, PJ;
Publicação
DISCOVERY SCIENCE, PROCEEDINGS
Abstract
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 chi(2). We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.
2005
Autores
Cunha, A;
Publicação
Abstract
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