Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/12775 |
Resumo: | The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator -based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization. |
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Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithmsConvex hullClassificationEvolutionary multiobjective optimizationParsimonyROC analysisAnti-spam filtersThe receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator -based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization.Elsevier2017-04-05T15:09:17Z2016-01-01T00:00:00Z20162019-04-12T11:37:08Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/12775eng0020-025510.1016/j.ins.2016.05.026Zhao, J.Basto-Fernandes, V.Jiao, L.Yevseyeva, I.Maulana, A.Li, R.Bäck, T.Tang, K.Emmerichd, M. T. M.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:51:29Zoai:repositorio.iscte-iul.pt:10071/12775Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:25:30.042651Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms |
title |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms |
spellingShingle |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms Zhao, J. Convex hull Classification Evolutionary multiobjective optimization Parsimony ROC analysis Anti-spam filters |
title_short |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms |
title_full |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms |
title_fullStr |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms |
title_full_unstemmed |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms |
title_sort |
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms |
author |
Zhao, J. |
author_facet |
Zhao, J. Basto-Fernandes, V. Jiao, L. Yevseyeva, I. Maulana, A. Li, R. Bäck, T. Tang, K. Emmerichd, M. T. M. |
author_role |
author |
author2 |
Basto-Fernandes, V. Jiao, L. Yevseyeva, I. Maulana, A. Li, R. Bäck, T. Tang, K. Emmerichd, M. T. M. |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Zhao, J. Basto-Fernandes, V. Jiao, L. Yevseyeva, I. Maulana, A. Li, R. Bäck, T. Tang, K. Emmerichd, M. T. M. |
dc.subject.por.fl_str_mv |
Convex hull Classification Evolutionary multiobjective optimization Parsimony ROC analysis Anti-spam filters |
topic |
Convex hull Classification Evolutionary multiobjective optimization Parsimony ROC analysis Anti-spam filters |
description |
The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator -based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01T00:00:00Z 2016 2017-04-05T15:09:17Z 2019-04-12T11:37:08Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/12775 |
url |
http://hdl.handle.net/10071/12775 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0020-0255 10.1016/j.ins.2016.05.026 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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