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dc.contributor.authorZhao, Jiaqien
dc.contributor.authorJiao, Lichengen
dc.contributor.authorLiu, Fangen
dc.contributor.authorFernandes, Vitor Bastoen
dc.contributor.authorYevseyeva, Irynaen
dc.contributor.authorXia, Shixongen
dc.contributor.authorEmmerich, Michael T. M.en
dc.date.accessioned2018-04-11T09:54:49Z
dc.date.available2018-04-11T09:54:49Z
dc.date.issued2018-06
dc.identifier.citationJiaqi Zhao, Licheng Jiao, Fang Liu, Vitor Basto Fernandes, Iryna Yevseyeva, Shixiong Xia, Michael T.M. Emmerich, (2018) 3D fast convex-hull-based evolutionary multiobjective optimization algorithm, Applied Soft Computing, Volume 67, June 2018, Pages 322-336.en
dc.identifier.urihttp://hdl.handle.net/2086/15932
dc.description.abstractThe receiver operating characteristic (ROC) and detection error tradeoff (DET) curves have been widely used in the machine learning community to analyze the performance of classifiers. The area (or volume) under the convex hull has been used as a scalar indicator for the performance of a set of classifiers in ROC and DET space. Recently, 3D convex-hull-based evolutionary multiobjective optimization algorithm (3DCH-EMOA) has been proposed to maximize the volume of convex hull for binary classification combined with parsimony and three-way classification problems. However, 3DCH-EMOA revealed high consumption of computational resources due to redundant convex hull calculations and a frequent execution of nondominated sorting. In this paper, we introduce incremental convex hull calculation and a fast replacement for non-dominated sorting. While achieving the same high quality results, the computational effort of 3DCH-EMOA can be reduced by orders of magnitude. The average time complexity of 3DCH-EMOA in each generation is reduced from O ( n 2 log n ) to O ( n log n ) per iteration, where n is the population size. Six test function problems are used to test the performance of the newly proposed method, and the algorithms are compared to several state-of-the-art algorithms, including NSGA-III, RVEA, etc., which were not compared to 3DCH-EMOA before. Experimental results show that the new version of the algorithm (3DFCH-EMOA) can speed up 3DCH-EMOA for about 30 times for a typical population size of 300 without reducing the performance of the method. Besides, the proposed algorithm is applied for neural networks pruning, and several UCI datasets are used to test the performance.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectConvex hull; Area under ROC; Indicator-based evolutionary algorithm; Multiobjective optimization; ROC analysisen
dc.title3D fast convex-hull-based evolutionary multiobjective optimization algorithmen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2018.03.005
dc.researchgroupCyber Technology Institute (CTI)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2018-03-03en
dc.researchinstituteCyber Technology Institute (CTI)en


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