An ensemble approach of dual base learners for multi-class classification problems. Sesmero, M., Alonso-Weber, J. M., Gutierrez, G., Ledezma, A., & Sanchis, A. Information Fusion, 24:122–136, 2015. ![An ensemble approach of dual base learners for multi-class classification problems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient.
@article{Sesmero2015,
abstract = {In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient.},
author = {Sesmero, M.P. and Alonso-Weber, Juan M. and Gutierrez, German and Ledezma, A. and Sanchis, A.},
doi = {doi:10.1016/j.inffus.2014.09.002},
file = {:C$\backslash$:/Paz/Articulos/InformationFusion/Sesmero2015b.pdf:pdf},
issn = {1566-2535},
journal = {Information Fusion},
keywords = {Artificial Neural Networks,Diversity,Ensemble of classifiers,Feature Selection,Multi-class classification},
pages = {122--136},
title = {{An ensemble approach of dual base learners for multi-class classification problems}},
url = {http://www.sciencedirect.com/science/article/pii/S156625351400102X},
volume = {24},
year = {2015}
}
Downloads: 0
{"_id":"7kKDMBKQujpfixdfQ","authorIDs":[],"author_short":["Sesmero, M.","Alonso-Weber, J. M.","Gutierrez, G.","Ledezma, A.","Sanchis, A."],"bibbaseid":"sesmero-alonsoweber-gutierrez-ledezma-sanchis-anensembleapproachofdualbaselearnersformulticlassclassificationproblems-2015","bibdata":{"bibtype":"article","type":"article","abstract":"In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient.","author":[{"propositions":[],"lastnames":["Sesmero"],"firstnames":["M.P."],"suffixes":[]},{"propositions":[],"lastnames":["Alonso-Weber"],"firstnames":["Juan","M."],"suffixes":[]},{"propositions":[],"lastnames":["Gutierrez"],"firstnames":["German"],"suffixes":[]},{"propositions":[],"lastnames":["Ledezma"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Sanchis"],"firstnames":["A."],"suffixes":[]}],"doi":"doi:10.1016/j.inffus.2014.09.002","file":":C$\\backslash$:/Paz/Articulos/InformationFusion/Sesmero2015b.pdf:pdf","issn":"1566-2535","journal":"Information Fusion","keywords":"Artificial Neural Networks,Diversity,Ensemble of classifiers,Feature Selection,Multi-class classification","pages":"122–136","title":"An ensemble approach of dual base learners for multi-class classification problems","url":"http://www.sciencedirect.com/science/article/pii/S156625351400102X","volume":"24","year":"2015","bibtex":"@article{Sesmero2015,\r\nabstract = {In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient.},\r\nauthor = {Sesmero, M.P. and Alonso-Weber, Juan M. and Gutierrez, German and Ledezma, A. and Sanchis, A.},\r\ndoi = {doi:10.1016/j.inffus.2014.09.002},\r\nfile = {:C$\\backslash$:/Paz/Articulos/InformationFusion/Sesmero2015b.pdf:pdf},\r\nissn = {1566-2535},\r\njournal = {Information Fusion},\r\nkeywords = {Artificial Neural Networks,Diversity,Ensemble of classifiers,Feature Selection,Multi-class classification},\r\npages = {122--136},\r\ntitle = {{An ensemble approach of dual base learners for multi-class classification problems}},\r\nurl = {http://www.sciencedirect.com/science/article/pii/S156625351400102X},\r\nvolume = {24},\r\nyear = {2015}\r\n}\r\n\r\n\r\n","author_short":["Sesmero, M.","Alonso-Weber, J. M.","Gutierrez, G.","Ledezma, A.","Sanchis, A."],"key":"Sesmero2015","id":"Sesmero2015","bibbaseid":"sesmero-alonsoweber-gutierrez-ledezma-sanchis-anensembleapproachofdualbaselearnersformulticlassclassificationproblems-2015","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S156625351400102X"},"keyword":["Artificial Neural Networks","Diversity","Ensemble of classifiers","Feature Selection","Multi-class classification"],"downloads":0,"html":"","metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"http://www.caos.inf.uc3m.es/bibs/pubCAOS.bib","creationDate":"2015-02-25T11:30:38.925Z","downloads":0,"keywords":["artificial neural networks","diversity","ensemble of classifiers","feature selection","multi-class classification"],"search_terms":["ensemble","approach","dual","base","learners","multi","class","classification","problems","sesmero","alonso-weber","gutierrez","ledezma","sanchis"],"title":"An ensemble approach of dual base learners for multi-class classification problems","year":2015,"dataSources":["Z4T4fewybNpYCWveD"]}