Scopus
YÖKSİS DOI Eşleşti
SJR Q1
A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA
Neural Computing and Applications · Eylül 2013
YÖKSİS Kayıtları
Hybrid breast cancer detection tem via neural network and feature ion based on SBS SFS and PCA
NEURAL COMPUTING & APPLICATIONS · 2013 SCI-Expanded 5 atıf
Doç. Dr. MUSTAFA SERTER UZER →
Hybrid breast cancer detection tem via neural network and feature ion based on SBS SFS and PCA
NEURAL COMPUTING & APPLICATIONS · 2013 SCI-Expanded 5 atıf
Dr. Öğr. Üyesi ONUR İNAN →
A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA
Neural Computing and Applications · 2013 SCI-Expanded
Doç. Dr. MUSTAFA SERTER UZER →
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Short term load forecasting using fuzzy logic and ANFIS
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2013 ISSN: 0941-0643 SCI-Expanded
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Determination of induction motor parameters with differential evolution algorithm
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Cost optimization of mixed feeds with the particle swarm optimization method
2013 ISSN: 0941-0643 SCI-Expanded
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Fuzzy logic based induction motor protection system
2013 ISSN: 0941-0643 SCI
Dr. Öğr. Üyesi OKAN UYAR →
A new MILP model proposal in feed formulation and using a hybrid linear binary PSO H LBP approach for alternative solutions
2018 ISSN: 0941-0643 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
FPGA based self organizing fuzzy controller for electromagnetic filter
2016 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
A new MILP model proposal in feed formulation and using a hybrid linear binary PSO H LBP approach for alternative solutions
2016 ISSN: 0941-0643 SCI-Expanded
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2018 ISSN: 0941-0643 SCI-Expanded Q1
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
A new denoising method for fMRI based on weighted three-dimentional wavelet transform
2018 ISSN: 0941-0643 SCI-Expanded
Dr. Öğr. Üyesi GÜZİN ÖZMEN →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
A new MILP model proposal in feed formulation and using a hybrid-linear binary PSO (H-LBP) approach for alternative solutions
2018 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
Hybrid breast cancer detection tem via neural network and feature ion based on SBS SFS and PCA
2013 ISSN: 0941-0643 SCI-Expanded 5 atıf
Dr. Öğr. Üyesi ONUR İNAN →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
Makale Bilgileri
ISSN09410643
Yayın TarihiEylül 2013
Cilt / Sayfa23 · 719-728
Scopus ID2-s2.0-84884596718
Özet
Two hybrid feature selection methods (SFSP and SBSP) which are composed by combining the sequential forward selection and the sequential backward selection together with the principal component analysis developed by utilizing quadratic discriminant analysis classification algorithmic criteria so as to utilize in the diagnosis of breast cancer fast and effectively are presented in this study. The tenfold cross-validation method has been applied in the algorithm, which is utilized as criteria during the selection of the features. The dimension of the feature space for input has been decreased from 9 to 4 thanks to the selection of these two hybrid features. The Artificial Neural Networks have been used as classifier. The cross-validation method has been preferred also in the phase of this classification as in the case of the selection of the feature in order to increase the reliability of the result. The Wisconsin Breast Cancer Database obtained from the UCI has been utilized so as to determine the correctness of the system suggested. The values of the average correctness of the classification obtained by utilizing a tenfold cross-validation of the two hybrid systems developed earlier are found, respectively, as follows: for SFSP + NN, 97.57 % and for SBSP + NN, 98.57 %. SBSP + NN system has been observed that, among the studies carried out by implementing the cross-validation method for the breast cancer, the result appears to be very promising. The acquired results have revealed that this hybrid system applied by means of reducing dimension is an utilizable system in order to diagnose the diseases faster and more successfully. © 2012 Springer-Verlag London Limited.
Yazarlar (3)
1
Mustafa Serter Uzer
ORCID: 0000-0002-8829-5987
2
Onur Inan
3
Nihat Yilmaz
Anahtar Kelimeler
Breast cancer diagnosis
Feature selection
Neural network
PCA
SBS
SFS
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Neural Computing and Applications
Q1
SJR Skoru1,102
H-Index146
YayıncıSpringer London
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Software (Q1)
Metrikler
39
Atıf
3
Yazar
6
Anahtar Kelime