A new hybrid feature selection method based on association rules and pca for detection of breast cancer
Küçük Resim Yok
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, a new hybrid feature selection method named as AP has been formed to detect breast cancer, using association rules (Apriori algorithm) and Principal Component Analysis (PCA) together with artificial neural network classifier. Thanks to this hybrid system, both the decrease in the size of data and the successful and fast training of classifiers have been achieved. In order to detect the accuracy of the suggested system, Wisconsin breast cancer data have been used. 10-fold cross-validation has been used on the classification phase. The average classification accuracy of the developed AP + NN system is 98.29%. Among the studies performed through cross-validation method for breast cancer, our study result appears to be very promising. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster and more accurate diagnosis of diseases. © 2013 ICIC International.
Açıklama
Anahtar Kelimeler
Apriori, Breast cancer diagnosis, Feature selection, Neural network, PCA
Kaynak
International Journal of Innovative Computing, Information and Control
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
9
Sayı
2
