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Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

2016-11Journal article Open access
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dc.contributor.authorZhao, Jiaqi
dc.contributor.authorBasto-Fernandes, Vitor
dc.contributor.authorJiao, Licheng
dc.contributor.authorYevseyeva, Iryna
dc.contributor.authorMaulana, Asep
dc.contributor.authorLi, Rui
dc.contributor.authorBäck, Thomas
dc.contributor.authorTang, Ke
dc.contributor.authorEmmerich, Michael T.M.
dc.date.accessioned2025-05-28T15:32:09Z
dc.date.available2025-05-28T15:32:09Z
dc.date.issued2016-11
dc.description.abstractThe 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.por
dc.description.sponsorshipThis work was partially supported by the National Basic Research Program (973 Program) of China (no. 2013CB329402), the National Natural Science Foundation of China (nos. 61473215, 61371201, 61373111, 61303032, 61271301, 61272279, 61203303 and 61571342), the Excellent Young Science Foundation of China (no. 61522311), the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (no. B07048), the Major Research Plan of the National Natural Science Foundation of China (nos. 91438201 and 91438103), the Program for Cheung Kong Scholars and Innovative Research Team in University (no. IRT_15R53), and the European Union Seventh Framework Programme under Grant agreements (no. 247619) on Nature Inspired Computation and its Applications (NICaiA). Asep Maulana thanks for financial support by the Indonesian Endowment Grant.
dc.identifier.citationJiaqi Zhao, Vitor Basto Fernandes, Licheng Jiao, Iryna Yevseyeva, Asep Maulana, Rui Li, Thomas Bäck, Ke Tang, Michael T.M. Emmerich, Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms, Information Sciences, Volumes 367–368, 2016, Pages 80-104, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2016.05.026.
dc.identifier.doi10.1016/j.ins.2016.05.026
dc.identifier.issn0020-0255
dc.identifier.urihttp://hdl.handle.net/10400.8/13022
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier BV
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S0020025516303504
dc.relation.ispartofInformation Sciences
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectConvex hull
dc.subjectClassification
dc.subjectEvolutionary multiobjective optimization
dc.subjectParsimony
dc.subjectROC analysis
dc.subjectAnti-spam filters
dc.titleMultiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithmseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage104
oaire.citation.startPage80
oaire.citation.titleJournal of Information Science
oaire.citation.volume367-368
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBasto-Fernandes
person.givenNameVitor
person.identifier.ciencia-id581C-52BB-AC4E
person.identifier.orcid0000-0003-4269-5114
person.identifier.ridN-1891-2016
person.identifier.scopus-author-id53363129900
relation.isAuthorOfPublicationfb2d3703-9d6a-4c22-bbc4-9ff14c162feb
relation.isAuthorOfPublication.latestForDiscoveryfb2d3703-9d6a-4c22-bbc4-9ff14c162feb

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