Performance Evaluation of Classification Algorithms Using Hyperparameter Optimization

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dc.authorscopusid57210945930
dc.authorscopusid36862932600
dc.contributor.authorKarcioglu A.A.
dc.contributor.authorBulut H.
dc.date.accessioned2023-01-12T20:23:23Z
dc.date.available2023-01-12T20:23:23Z
dc.date.issued2021
dc.departmentN/A/Departmenten_US
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826en_US
dc.description.abstractClassification problems have an important role in the field of machine learning and data mining. Classification problems are used in different areas such as disease diagnosis, estimation of bank customers, drug studies, sentiment analysis. Many classification algorithms have been developed in the literature and these algorithms have many different parameter inputs. In this study, it is aimed to increase the classification success by using hyperparameter optimization algorithms. K-nearest neighbor, support vector machines, decision tree and gradient boosting classification algorithms were applied to the frequently used 'heart and iris' datasets in the literature. Grid search and random search algorithms, which are hyperparameter optimization algorithms, are applied to these selected classification algorithms. As a result of the experimental studies, it has been observed that the accuracy of all classification algorithms increases when hyperparameter optimization algorithms are applied. The parameter values that give the best results are shown. © 2021 IEEEen_US
dc.identifier.doi10.1109/UBMK52708.2021.9559003
dc.identifier.endpage358en_US
dc.identifier.isbn9781665429085
dc.identifier.scopus2-s2.0-85125837162en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage354en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9559003
dc.identifier.urihttps://hdl.handle.net/11454/79696
dc.indekslendigikaynakScopusen_US
dc.language.isotren_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.subjectClassification Algorithmsen_US
dc.subjectHyperparameter Optimizationen_US
dc.subjectMachine Learningen_US
dc.subjectOptimizationen_US
dc.subjectSupervised Learningen_US
dc.subjectComputer aided diagnosisen_US
dc.subjectData miningen_US
dc.subjectDecision treesen_US
dc.subjectNearest neighbor searchen_US
dc.subjectSentiment analysisen_US
dc.subjectSupport vector machinesen_US
dc.subjectClassification algorithmen_US
dc.subjectDisease diagnosisen_US
dc.subjectHyper-parameter optimizationsen_US
dc.subjectMachine-learningen_US
dc.subjectNearest-neighbouren_US
dc.subjectOptimisationsen_US
dc.subjectOptimization algorithmsen_US
dc.subjectPerformances evaluationen_US
dc.subjectSentiment analysisen_US
dc.subjectSupervised learningen_US
dc.subjectClassification (of information)en_US
dc.titlePerformance Evaluation of Classification Algorithms Using Hyperparameter Optimizationen_US
dc.title.alternativeHiperparametre Optimizasyonu Yöntemleri Kullanilarak Siniflandirma Algoritmalannin Performans Degerlendirmesien_US
dc.typeConference Objecten_US

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