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Abstract: Ensemble techniques have proved to be very useful to boost the performance of several types of machine learning methods. In this paper we illustrate its usefulness in combination with GAssist, a Pittsburgh-style Learning Classifier System. Two types of ensemble are tested. First bagging-style consensus prediction. Second an ensemble intended to deal more e#ciently with ordinal classification problems. Both methods improve the performance and behaviour of GAssist in the tested domains. (Update)
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BibTeX entry: (Update)
@misc{ bacardit-empirical,
author = "Jaume Bacardit and Natalio Krasnogor",
title = "Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier
System",
url = "citeseer.ist.psu.edu/bacardit06empirical.html" }
Citations (may not include all citations):696 UCI repository of machine learning databases (context) - Blake, Keogh et al. - 1998
657 Bagging predictors - Breiman - 1996 ACM DBLP
500 Experiments with a new boosting algorithm - Freund, Schapire - 1996 DBLP
51 Using genetic algorithms for concept learning - DeJong, Spears et al. - 1993 ACM DBLP
7 Pittsburgh Genetics-Based Machine Learning in the Data Minin.. (context) - Bacardit - 2004
4 Analysis of the initialization stage of a pittsburgh approac.. (context) - Bacardit - 2005
4 The bottom line for prediction of residue solvent accessibil.. (context) - Richardson, Barlow - 1999
3 Data Mining and Knowledge Discovery (context) - Liu, Hussain et al. - 2002 ACM
2 A simple approach to ordinal classification - Frank, Hall - 2001 ACM DBLP
1 Special issue on integrating multiple learned models (context) - authors - 1999
1 Where to go once you have evolved a bunch of promising hypot.. (context) - Llora, Bacardit et al. - 2006
1 the use of rule sharing in learning classifier system ensemb.. (context) - Bull, Studley et al. - 2005
1 Coordination number prediction using learning classifier sys.. - Bacardit, Stout et al. - 2006
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