An empirical demonstration of the no free lunch theorem

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dc.contributor.authorOgundepo, Ezekiel Adebayo
dc.contributor.authorFokoué, Ernest
dc.coverage.issue2cs
dc.coverage.volume8cs
dc.date.accessioned2020-05-05T06:21:04Z
dc.date.available2020-05-05T06:21:04Z
dc.date.issued2019cs
dc.description.abstractIn this paper, we provide a substantial empirical demonstration of thestatistical machine learning result known as the No Free Lunch Theorem (NFLT).We specifically compare the predictive performances of a wide variety of machinelearning algorithms/methods on a wide variety of qualitatively and quantitativelydifferent datasets. Our research work conclusively demonstrates a great evidence infavor of the NFLT by using an overall ranking of methods and their correspondinglearning machines, revealing in effect thatnone of the learning machines consideredpredictively outperforms all the other machines on all the widely different datasetsanalyzed. It is noteworthy however that while evidence from various datasets andmethods support the NFLT somewhat emphatically, some learning machines likeRandom Forest, Adaptive Boosting, and Support Vector Machines (SVM) appearto emerge as methods with the overall tendency to yield predictive performancesalmost always among the best.en
dc.formattextcs
dc.format.extent173-188cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMathematics for Applications. 2019 vol. 8, č. 2, s. 173-188. ISSN 1805-3629cs
dc.identifier.doi10.13164/ma.2019.11en
dc.identifier.issn1805-3629
dc.identifier.urihttp://hdl.handle.net/11012/186971
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.relation.ispartofMathematics for Applicationsen
dc.relation.urihttp://ma.fme.vutbr.cz/archiv/8_2/ma_8_2_5_ogundepo_fokoue_final.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.rights.accessopenAccessen
dc.titleAn empirical demonstration of the no free lunch theoremen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentÚstav matematikycs
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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