Genetic approach for optimizing ensemble of classifiers. Ordoñez, F. J., Ledezma, A., & Sanchis, A. In Wilson, D. & Lane, H. C., editors, Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference - FLAIRS 2008, pages 89–94, Coconut Grove, Florida, USA, May, 2008. AAAI Press. abstract bibtex An ensemble of classifiers is a set of classifiers whose predictions are combined in some way to classify new instances. Early research has shown that, in general, an ensemble of classifiers is more accurate than any of the single classifiers in the ensemble. Usually the gains obtained by combining different classifiers are more affected by the chosen classifiers than by the used combination. It is common in the research on this topic to select by hand the right combination of classifiers and the method to combine them, but the approach presented in this work uses genetic algorithms for selecting the classifiers and the combination method to use. Our approach, GA-Ensemble, is inspired by a previous work, called GA-Stacking. GA-Stacking is a method that uses genetic algorithms to find domain-specific Stacking configurations. The main goal of this work is to improve the efficiency of GA-Stacking and to compare GA-Ensemble with current ensemble building techniques. Preliminary results have show that the approach finds ensembles of classifiers whose performance is as good as the best techniques, without having to set up manually the classifiers and the ensemble method.
@INPROCEEDINGS{gaensemble-flairs08,
author = {Francisco Javier Ordoñez and Agapito Ledezma and Araceli Sanchis},
title = {Genetic approach for optimizing ensemble of classifiers},
booktitle = {Proceedings of the 21th International Florida Artificial Intelligence
Research Society Conference - FLAIRS 2008},
year = {2008},
editor = {David Wilson and H. Chad Lane},
pages = {89--94},
address = {Coconut Grove, Florida, USA},
month = {May},
publisher = {AAAI Press},
abstract = {An ensemble of classifiers is a set of classifiers whose predictions
are combined in some way to classify new instances. Early research
has shown that, in general, an ensemble of classifiers is more accurate
than any of the single classifiers in the ensemble. Usually the gains
obtained by combining different classifiers are more affected by
the chosen classifiers than by the used combination. It is common
in the research on this topic to select by hand the right combination
of classifiers and the method to combine them, but the approach presented
in this work uses genetic algorithms for selecting the classifiers
and the combination method to use. Our approach, GA-Ensemble, is
inspired by a previous work, called GA-Stacking. GA-Stacking is a
method that uses genetic algorithms to find domain-specific Stacking
configurations. The main goal of this work is to improve the efficiency
of GA-Stacking and to compare GA-Ensemble with current ensemble building
techniques. Preliminary results have show that the approach finds
ensembles of classifiers whose performance is as good as the best
techniques, without having to set up manually the classifiers and
the ensemble method.},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Ensemble of Classifiers},
days = {15--17},
isbn = {978-1-57735-365-2},
owner = {ledezma},
timestamp = {2008.07.22}
}
Downloads: 0
{"_id":{"_str":"53c7994316cace54670004f0"},"__v":0,"authorIDs":["54576e722abc8e9f370003c4","8mdzoMZ27yguKiBHc"],"author_short":["Ordoñez, F. J.","Ledezma, A.","Sanchis, A."],"bibbaseid":"ordoez-ledezma-sanchis-geneticapproachforoptimizingensembleofclassifiers-2008","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Francisco","Javier"],"propositions":[],"lastnames":["Ordoñez"],"suffixes":[]},{"firstnames":["Agapito"],"propositions":[],"lastnames":["Ledezma"],"suffixes":[]},{"firstnames":["Araceli"],"propositions":[],"lastnames":["Sanchis"],"suffixes":[]}],"title":"Genetic approach for optimizing ensemble of classifiers","booktitle":"Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference - FLAIRS 2008","year":"2008","editor":[{"firstnames":["David"],"propositions":[],"lastnames":["Wilson"],"suffixes":[]},{"firstnames":["H.","Chad"],"propositions":[],"lastnames":["Lane"],"suffixes":[]}],"pages":"89–94","address":"Coconut Grove, Florida, USA","month":"May","publisher":"AAAI Press","abstract":"An ensemble of classifiers is a set of classifiers whose predictions are combined in some way to classify new instances. Early research has shown that, in general, an ensemble of classifiers is more accurate than any of the single classifiers in the ensemble. Usually the gains obtained by combining different classifiers are more affected by the chosen classifiers than by the used combination. It is common in the research on this topic to select by hand the right combination of classifiers and the method to combine them, but the approach presented in this work uses genetic algorithms for selecting the classifiers and the combination method to use. Our approach, GA-Ensemble, is inspired by a previous work, called GA-Stacking. GA-Stacking is a method that uses genetic algorithms to find domain-specific Stacking configurations. The main goal of this work is to improve the efficiency of GA-Stacking and to compare GA-Ensemble with current ensemble building techniques. Preliminary results have show that the approach finds ensembles of classifiers whose performance is as good as the best techniques, without having to set up manually the classifiers and the ensemble method.","bib2html_pubtype":"Refereed Conference","bib2html_rescat":"Ensemble of Classifiers","days":"15–17","isbn":"978-1-57735-365-2","owner":"ledezma","timestamp":"2008.07.22","bibtex":"@INPROCEEDINGS{gaensemble-flairs08,\n author = {Francisco Javier Ordoñez and Agapito Ledezma and Araceli Sanchis},\n title = {Genetic approach for optimizing ensemble of classifiers},\n booktitle = {Proceedings of the 21th International Florida Artificial Intelligence\n\tResearch Society Conference - FLAIRS 2008},\n year = {2008},\n editor = {David Wilson and H. Chad Lane},\n pages = {89--94},\n address = {Coconut Grove, Florida, USA},\n month = {May},\n publisher = {AAAI Press},\n abstract = {An ensemble of classifiers is a set of classifiers whose predictions\n\tare combined in some way to classify new instances. Early research\n\thas shown that, in general, an ensemble of classifiers is more accurate\n\tthan any of the single classifiers in the ensemble. Usually the gains\n\tobtained by combining different classifiers are more affected by\n\tthe chosen classifiers than by the used combination. It is common\n\tin the research on this topic to select by hand the right combination\n\tof classifiers and the method to combine them, but the approach presented\n\tin this work uses genetic algorithms for selecting the classifiers\n\tand the combination method to use. Our approach, GA-Ensemble, is\n\tinspired by a previous work, called GA-Stacking. GA-Stacking is a\n\tmethod that uses genetic algorithms to find domain-specific Stacking\n\tconfigurations. The main goal of this work is to improve the efficiency\n\tof GA-Stacking and to compare GA-Ensemble with current ensemble building\n\ttechniques. Preliminary results have show that the approach finds\n\tensembles of classifiers whose performance is as good as the best\n\ttechniques, without having to set up manually the classifiers and\n\tthe ensemble method.},\n bib2html_pubtype = {Refereed Conference},\n bib2html_rescat = {Ensemble of Classifiers},\n days = {15--17},\n isbn = {978-1-57735-365-2},\n owner = {ledezma},\n timestamp = {2008.07.22}\n}\n\n","author_short":["Ordoñez, F. J.","Ledezma, A.","Sanchis, A."],"editor_short":["Wilson, D.","Lane, H. C."],"key":"gaensemble-flairs08","id":"gaensemble-flairs08","bibbaseid":"ordoez-ledezma-sanchis-geneticapproachforoptimizingensembleofclassifiers-2008","role":"author","urls":{},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"http://www.caos.inf.uc3m.es/bibs/pubAGAPITO.bib","creationDate":"2014-07-17T09:37:07.753Z","downloads":0,"keywords":[],"search_terms":["genetic","approach","optimizing","ensemble","classifiers","ordoñez","ledezma","sanchis"],"title":"Genetic approach for optimizing ensemble of classifiers","year":2008,"dataSources":["qbzEZxSSyFKDCos4s"]}