Initial Seed Value Effectiveness on Performances of Data Mining Algorithms

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dc.contributor.authorTimuçin, Tunahan
dc.contributor.authorArgun, İrem Düzdar
dc.date.accessioned2023-07-26T11:57:44Z
dc.date.available2023-07-26T11:57:44Z
dc.date.issued2021
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentDÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractAfter 2000s, Computer capacities and features are increased and access to data made easy. However, the produced and recorded data should be meaningful. Transformation of unprocessed data into meaningful information can be done with the help of data mining. In this study, classification methods from data mining applications are studied. First, the parameters that make the results of the same data set different were investigated on 4 different data mining tools (Weka, Rapid Miner, Knime, Orange), It has been tested with 3 different algorithms (K nearest neighborhood, Naive Bayes, Random Forest). In order to evaluate the performance of the data set while creating the classification models, the data set was divided into training data and test data as 80% -20%, 70% -30% and 60-40%. The accuracy, roc and precision values was used to test the performance of the classifying data. While classifying, the effect of algorithm parameters on the results is observed. The most important of these parameters is the initial seed value. The initial seed is a value using especially in classification algorithms that determines the initial placement of the data and directly affects the result. In this respect, it is very important to determine the initial seed value correctly. In this study, initial seed values between 0 and 100 were evaluated and it was shown that the classification could change the accuracy value approximately by 5%.en_US
dc.identifier.doi10.29130/dubited.813101
dc.identifier.endpage567en_US
dc.identifier.issn2148-2446
dc.identifier.issue2en_US
dc.identifier.startpage555en_US
dc.identifier.trdizinid496787en_US
dc.identifier.urihttp://doi.org/10.29130/dubited.813101
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/496787
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13290
dc.identifier.volume9en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorTimuçin, Tunahan
dc.institutionauthorArgun, İrem Düzdar
dc.language.isoenen_US
dc.relation.ispartofDüzce Üniversitesi Bilim ve Teknoloji Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCredit approvalen_US
dc.subjectSeed valueen_US
dc.subjectclassificationen_US
dc.subjectdata mining Kredi onayıen_US
dc.subjectsınıflamaen_US
dc.subjectTohum değerien_US
dc.subjectveri madenciliğien_US
dc.titleInitial Seed Value Effectiveness on Performances of Data Mining Algorithmsen_US
dc.typeArticleen_US

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