Feature Selection with Evolving, Fast and Slow Using Two Parallel Genetic Algorithms

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dc.contributor.authorCetin, Uzay
dc.contributor.authorGundogmus, Yunus Emre
dc.date.accessioned2024-07-18T20:47:28Z
dc.date.available2024-07-18T20:47:28Z
dc.date.issued2019
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description4th International Conference on Computer Science and Engineering (UBMK) -- SEP 11-15, 2019 -- Samsun, TURKEYen_US
dc.description.abstractFeature selection is one of the most challenging issues in machine learning, especially while working with high dimensional data. In this paper, we address the problem of feature selection and propose a new approach called Evolving Fast and Slow. This new approach is based on using two parallel genetic algorithms having high and low mutation rates, respectively. Evolving Fast and Slow requires a new parallel architecture combining an automatic system that evolves fast and an effortful system that evolves slow. With this architecture, exploration and exploitation can be done simultaneously and in unison. Evolving last, with high mutation rate, can be useful to explore new unknown places in the search space with long jumps; and Evolving Slow, with low mutation rate, can be useful to exploit previously known places in the search space with short movements. Our experiments show that Evolving Fast and Slow achieves very good results in terms of both accuracy and feature elimination.en_US
dc.description.sponsorshipIEEE,IEEE Turkey Secten_US
dc.description.sponsorshipUzay Cetinen_US
dc.description.sponsorshipThis study is accomplished as a term project of Free Artificial Intelligence Course to Young People in Sariyer Akademi, given by Uzay Cetin [13]. We thank Sariyer Akademi and Sami Gorey for their support [12].en_US
dc.identifier.doi10.1109/ubmk.2019.8907165
dc.identifier.endpage703en_US
dc.identifier.isbn978-1-7281-3964-7
dc.identifier.scopus2-s2.0-85076212667en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage699en_US
dc.identifier.urihttps://doi.org/10.1109/ubmk.2019.8907165
dc.identifier.urihttps://hdl.handle.net/11411/7801
dc.identifier.wosWOS:000609879900132en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2019 4th International Conference on Computer Science and Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature Selectionen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectHigh Di-Mensional Dataen_US
dc.subjectDistributed Computationen_US
dc.subjectMachine Learningen_US
dc.titleFeature Selection with Evolving, Fast and Slow Using Two Parallel Genetic Algorithmsen_US
dc.typeConference Objecten_US

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