Training set selection and swarm intelligence for enhanced integration in multiple classifier systems

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dc.contributor.authorElsayed, Amgad Monir Mohamed
dc.contributor.authorOnieva Caracuel, Enrique
dc.contributor.authorWoźniak, Michał
dc.date.accessioned2024-11-22T07:50:30Z
dc.date.available2024-11-22T07:50:30Z
dc.date.issued2020-10
dc.date.updated2024-11-22T07:50:30Z
dc.description.abstractMultiple classifier systems (MCSs) constitute one of the most competitive paradigms for obtaining more accurate predictions in the field of machine learning. Systems of this type should be designed efficiently in all of their stages, from data preprocessing to multioutput decision fusion. In this article, we present a framework for utilizing the power of instance selection methods and the search capabilities of swarm intelligence to train learning models and to aggregate their decisions. The process consists of three steps: First, the essence of the complete training data set is captured in a reduced set via the application of intelligent data sampling. Second, the reduced set is used to train a group of heterogeneous classifiers using bagging and distance-based feature sampling. Finally, swarm intelligence techniques are applied to identify a pattern among multiple decisions to enhance the fusion process by assigning class-specific weights for each classifier. The proposed methodology yielded competitive results in experiments that were conducted on 25 benchmark datasets. The Matthews correlation coefficient (MCC) is regarded as the objective to be maximized by various nature-inspired metaheuristics, which include the moth-flame optimization algorithm (MFO), the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA).en
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement N° 665959 . In addition, Michał Woźniak was supported by the Polish National Science Centre, Poland under the grant No. 2017/27/B/ST6/01325.en
dc.identifier.citationMohammed, A. M., Onieva, E., & Woźniak, M. (2020). Training set selection and swarm intelligence for enhanced integration in multiple classifier systems. Applied Soft Computing Journal, 95. https://doi.org/10.1016/J.ASOC.2020.106568
dc.identifier.doi10.1016/J.ASOC.2020.106568
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2071
dc.language.isoeng
dc.publisherElsevier Ltd
dc.subject.otherBig data
dc.subject.otherClassifier ensemble
dc.subject.otherClassifier integration
dc.subject.otherCombination rule
dc.subject.otherData reduction
dc.subject.otherMachine learning
dc.subject.otherMultiple classifier systems
dc.subject.otherOptimization
dc.subject.otherSwarm intelligence
dc.titleTraining set selection and swarm intelligence for enhanced integration in multiple classifier systemsen
dc.typejournal article
dcterms.accessRightsmetadata only access
oaire.citation.titleApplied Soft Computing Journal
oaire.citation.volume95
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