Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification
The Scientific World Journal · Eylül 2013
YÖKSİS Kayıtları
Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification
Scientific World Journal · 2013 SCI-Expanded 8 atıf
DOÇENT MUSTAFA SERTER UZER →
Makale Bilgileri
DergiThe Scientific World Journal
Yayın TarihiEylül 2013
Cilt / Sayfa2013
Scopus ID2-s2.0-84883204011
Erişim🔓 Açık Erişim
Özet
This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications. © 2013 Mustafa Serter Uzer et al.
Yazarlar (3)
1
Mustafa Serter Uzer
ORCID: 0000-0002-8829-5987
2
Nihat Yilmaz
3
Onur Inan
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
87
Atıf
3
Yazar