Ensembles of jittered association rule classifiers

The ensembling of classifiers tends to improve predictive accuracy. To obtain an ensemble with N classifiers, one typically needs to run N learning processes. In this paper we introduce and explore Model Jittering Ensembling, where one single model is perturbed in order to obtain variants that can be used as an ensemble. We use as base classifiers sets of classification association rules. The two methods of jittering ensembling we propose are Iterative Reordering Ensembling (IRE) and Post Bagging (PB). Both methods start by learning one rule set over a single run, and then produce multiple rule sets without relearning. Empirical results on 36 data sets are positive and show that both strategies tend to reduce error with respect to the single model association rule classifier. A bias–variance analysis reveals that while both IRE and PB are able to reduce the variance component of the error, IRE is particularly effective in reducing the bias component. We show that Model Jittering Ensembling can represent a very good speed-up w.r.t. multiple model learning ensembling. We also compare Model Jittering with various state of the art classifiers in terms of predictive accuracy and computational efficiency.This work was partially supported by FCT project Rank! (PTDC/EIA/81178/2006) and by AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER), and also supported by Fundacao Ciencia e Tecnologia, FEDER e Programa de Financiamento Plurianual de Unidades de I & D. Thanks are due to William Cohen for kindly providing the executable code for the SLIPPER implementation. Our gratitude goes also to our anonymous reviewers who have helped to significantly improve this paper by sharing their knowledge and their informed criticism with the authors

Efficient local search for Pseudo Boolean Optimization

Algorithms and the Foundations of Software technolog