GA-Stacking: Evolutionary Stacked Generalization. Ledezma, A., Aler, R., Sanchis, A., & Borrajo, D. Intelligent Data Analysis, IOS Press, 2009. Acceptedabstract bibtex Stacking is one of the most used techniques for combining classifiers and improve prediction accuracy. Early research in stacking showed that selecting the right classifiers, their parameters and the metaclassifiers was the main bottleneck for its use. Most of the research on this topic selects by hand the right combination of classifiers and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good stacking configurations. Since this can lead to overfitting, one of the goals of this paper is to evaluate empirically the overall efficiency of the approach. A second goal is to compare our approach with current best stacking building techniques. The results show that our approach finds stacking configurations that, in the worst case, perform as well as the best techniques, with the advantage of not having to set up manually the structure of the stacking system.
@ARTICLE{jida09-gastacking,
author = {Agapito Ledezma and Ricardo Aler and Araceli Sanchis and Daniel Borrajo},
title = {GA-Stacking: Evolutionary Stacked Generalization},
journal = {Intelligent Data Analysis},
year = {2009},
volume = {14},
note = {Accepted},
abstract = {Stacking is one of the most used techniques for combining classifiers
and improve prediction accuracy. Early research in stacking showed
that selecting the right classifiers, their parameters and the metaclassifiers
was the main bottleneck for its use. Most of the research on this
topic selects by hand the right combination of classifiers and their
parameters. Instead of starting from these initial strong assumptions,
our approach uses genetic algorithms to search for good stacking
configurations. Since this can lead to overfitting, one of the goals
of this paper is to evaluate empirically the overall efficiency of
the approach. A second goal is to compare our approach with current
best stacking building techniques. The
results show that our approach finds stacking configurations that,
in the worst case, perform as well as the best techniques, with the
advantage of not having to set up manually the structure of the stacking
system.},
bib2html_pubtype = {Journal},
bib2html_rescat = {Ensemble of classifiers},
country = {Netherlands},
issn = {1088-467X (Print) 1571-4128 (Online)},
jcr = {2007: 0.446 (76/93), 2008: 0.426 (86/94)},
jpublisher = {IOS Press},
owner = {ledezma},
publisher = {IOS Press},
timestamp = {2011.11.21}
}
Downloads: 0
{"_id":{"_str":"5342634f0e946d920a000d82"},"__v":16,"authorIDs":["5457199e2abc8e9f3700006b","54575aa32abc8e9f370002ec","54576e722abc8e9f370003c4","5469957ebc7d6a460d00140f"],"author_short":["Ledezma, A.","Aler, R.","Sanchis, A.","Borrajo, D."],"bibbaseid":"ledezma-aler-sanchis-borrajo-gastackingevolutionarystackedgeneralization-2009","bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Agapito"],"propositions":[],"lastnames":["Ledezma"],"suffixes":[]},{"firstnames":["Ricardo"],"propositions":[],"lastnames":["Aler"],"suffixes":[]},{"firstnames":["Araceli"],"propositions":[],"lastnames":["Sanchis"],"suffixes":[]},{"firstnames":["Daniel"],"propositions":[],"lastnames":["Borrajo"],"suffixes":[]}],"title":"GA-Stacking: Evolutionary Stacked Generalization","journal":"Intelligent Data Analysis","year":"2009","volume":"14","note":"Accepted","abstract":"Stacking is one of the most used techniques for combining classifiers and improve prediction accuracy. Early research in stacking showed that selecting the right classifiers, their parameters and the metaclassifiers was the main bottleneck for its use. Most of the research on this topic selects by hand the right combination of classifiers and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good stacking configurations. Since this can lead to overfitting, one of the goals of this paper is to evaluate empirically the overall efficiency of the approach. A second goal is to compare our approach with current best stacking building techniques. The results show that our approach finds stacking configurations that, in the worst case, perform as well as the best techniques, with the advantage of not having to set up manually the structure of the stacking system.","bib2html_pubtype":"Journal","bib2html_rescat":"Ensemble of classifiers","country":"Netherlands","issn":"1088-467X (Print) 1571-4128 (Online)","jcr":"2007: 0.446 (76/93), 2008: 0.426 (86/94)","jpublisher":"IOS Press","owner":"ledezma","publisher":"IOS Press","timestamp":"2011.11.21","bibtex":"@ARTICLE{jida09-gastacking,\r\n author = {Agapito Ledezma and Ricardo Aler and Araceli Sanchis and Daniel Borrajo},\r\n title = {GA-Stacking: Evolutionary Stacked Generalization},\r\n journal = {Intelligent Data Analysis},\r\n year = {2009},\r\n volume = {14},\r\n note = {Accepted},\r\n abstract = {Stacking is one of the most used techniques for combining classifiers\r\n\tand improve prediction accuracy. Early research in stacking showed\r\n\tthat selecting the right classifiers, their parameters and the metaclassifiers\r\n\twas the main bottleneck for its use. Most of the research on this\r\n\ttopic selects by hand the right combination of classifiers and their\r\n\tparameters. Instead of starting from these initial strong assumptions,\r\n\tour approach uses genetic algorithms to search for good stacking\r\n\tconfigurations. Since this can lead to overfitting, one of the goals\r\n\tof this paper is to evaluate empirically the overall efficiency of\r\n\tthe approach. A second goal is to compare our approach with current\r\n\tbest stacking building techniques. The\r\n\t\r\n\tresults show that our approach finds stacking configurations that,\r\n\tin the worst case, perform as well as the best techniques, with the\r\n\tadvantage of not having to set up manually the structure of the stacking\r\n\tsystem.},\r\n bib2html_pubtype = {Journal},\r\n bib2html_rescat = {Ensemble of classifiers},\r\n country = {Netherlands},\r\n issn = {1088-467X (Print) 1571-4128 (Online)},\r\n jcr = {2007: 0.446 (76/93), 2008: 0.426 (86/94)},\r\n jpublisher = {IOS Press},\r\n owner = {ledezma},\r\n publisher = {IOS Press},\r\n timestamp = {2011.11.21}\r\n}\r\n\r\n","author_short":["Ledezma, A.","Aler, R.","Sanchis, A.","Borrajo, D."],"key":"jida09-gastacking","id":"jida09-gastacking","bibbaseid":"ledezma-aler-sanchis-borrajo-gastackingevolutionarystackedgeneralization-2009","role":"author","urls":{},"downloads":0,"html":"","metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"http://www.caos.inf.uc3m.es/bibs/pubCAOS.bib","downloads":0,"keywords":[],"search_terms":["stacking","evolutionary","stacked","generalization","ledezma","aler","sanchis","borrajo"],"title":"GA-Stacking: Evolutionary Stacked Generalization","year":2009,"dataSources":["Z4T4fewybNpYCWveD"]}