An experiment with association rules and classification : post-bagging and conviction

摘要

In this paper we study a new technique we call post-bagging, which consists inresampling parts of a classification model rather then the data. We dothis with a particular kind of model: large sets of classification association rules, andin 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.