Analysis of Honey Production with Environmenta Variables

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dc.authorscopusid56557054300
dc.authorscopusid57478854100
dc.contributor.authorAtagün, Ercan
dc.contributor.authorAlbayrak, Ahmet
dc.date.accessioned2023-07-26T11:57:43Z
dc.date.available2023-07-26T11:57:43Z
dc.date.issued2021
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- 176826en_US
dc.description.abstractRegression algorithms are included in the supervised learning techniques of machine learning. Regression covers the operations of estimating the variable with the class label (output variable) by using the numerical values in a data with regression algorithms. When the desired performances cannot be achieved with the existing regression algorithms for a problem, Ensemble Learning models are applied. In the Ensemble Learning model, multiple predictive algorithms come together and aim to achieve a higher success than the success of an algorithm alone. In this study, honey production problem was estimated with Support vector machines. Multi-layer Perceptron Regressor, KNeighborsRegressor, Voting Regressor, RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, GradientBoostingRegressor and the results were compared. It was observed that the ensemble learning models increased the prediction success with the regression processes. © 2021 IEEEen_US
dc.identifier.doi10.1109/UBMK52708.2021.9558933
dc.identifier.endpage465en_US
dc.identifier.isbn9.78167E+12
dc.identifier.scopus2-s2.0-85125872219en_US
dc.identifier.startpage462en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9558933
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13281
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAtagün, Ercan
dc.institutionauthorAlbayrak, Ahmet
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectEnsemble learningen_US
dc.subjectMigratory beekeepingen_US
dc.subjectRegressionen_US
dc.subjectSupervised learningen_US
dc.subjectFood productsen_US
dc.subjectLearning algorithmsen_US
dc.subjectSupport vector machinesen_US
dc.subjectClass labelsen_US
dc.subjectEnsemble learningen_US
dc.subjectHoney productionen_US
dc.subjectLearning modelsen_US
dc.subjectMigratory beekeepingen_US
dc.subjectNumerical valuesen_US
dc.subjectOutput variablesen_US
dc.subjectPerformanceen_US
dc.subjectPredictive algorithmsen_US
dc.subjectRegression algorithmsen_US
dc.subjectRegression analysisen_US
dc.titleAnalysis of Honey Production with Environmenta Variablesen_US
dc.typeConference Objecten_US

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