Use of Kernel Functions in Artificial Immune Systems for the Nonlinear Classification Problems
Yükleniyor...
Dosyalar
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Due to the fact that there exist only a small number of complex systems in artificial immune systems (AISs) that solve nonlinear problems, there is a need to develop nonlinear AIS approaches that would be among the well-known solution methods. In this study, we developed a kernel-based AIS to compensate for this deficiency by providing a nonlinear structure via transformation of distance calculations in the clonal selection models of classical AIS to kernel space. Applications of the developed system were conducted on Statlog heart disease dataset, which was taken from the University of California, Irvine Machine-Learning Repository, and on Doppler sonograms to diagnose atherosclerosis disease. The system obtained a classification accuracy of 85.93% for the Statlog heart disease dataset, while it achieved a 99.09% classification success for the Doppler dataset. With these results, our system seems to be a potential solution method, and it may be considered as a suitable method for hard nonlinear classification problems.
Açıklama
5th IEEE International Special Topic Conference on Information Technology in Biomedicine -- OCT, 2006 -- Ioannina, GREECE
Anahtar Kelimeler
Artificial immune systems (AISs), classification, Doppler sonograms, nonlinear classification, Statlog heart disease
Kaynak
Ieee Transactions on Information Technology in Biomedicine
WoS Q Değeri
Q2
Scopus Q Değeri
N/A
Cilt
13
Sayı
4
Künye
Latifoğlu, F., Kara, S., Güneş, S., Özşen, S., (2009). Use of Kernel Functions in Artificial Immune Systems for the Nonlinear Classification Problems. Ieee Transactions on Information Technology in Biomedicine, 13(4), 621-628.
