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Siniflandirma Başarisinin Artirilmasi için Hibrit bir Yaklaşim

  • Gazi University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

Özet

There are many factors that affect the performance of classification. The volume, size, type of data and classification methods are the most obvious factors. For the exact same data set, it is possible to achieve different classification performance values by using different classification methods Hence, the development of classification models that are more accurate and applicable to many areas for classification problems has a great importance. In this work, a hybrid classification model combining Naïve Bayes, Perceptron and KNN is proposed. In this model, a novel parameter called Decision Function is used. The proposed decision function aims to increase the classification success by considering the classification results of the three algorithms The performance evaluation results show that the proposed decision function significantly improves the classification success.

Tercüme edilen katkı başlığıA hybrid approach for improving the classification performance
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2nd International Conference on Computer Science and Engineering, UBMK 2017
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar682-687
Sayfa sayısı6
ISBN (Elektronik)9781538609309
DOI'lar
Yayın durumuYayınlandı - 31 Eki 2017
Harici olarak yayınlandıEvet
Etkinlik2nd International Conference on Computer Science and Engineering, UBMK 2017 - Antalya, !!Turkey
Süre: 5 Eki 20178 Eki 2017

Yayın serisi

Adı2nd International Conference on Computer Science and Engineering, UBMK 2017

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???event.eventtypes.event.conference???2nd International Conference on Computer Science and Engineering, UBMK 2017
Ülke/Bölge!!Turkey
ŞehirAntalya
Periyot5/10/178/10/17

Keywords

  • Classification
  • Hybrid classifier
  • KNN
  • Naïve Bayes
  • Perceptron

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