Paper Info
Title
Use of kernel functions in artificial immune systems for the nonlinear classification problems
Abstract
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 thatwould 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.
Year
DOI
Venue
2009
10.1109/TITB.2009.2019637
IEEE Transactions on Information Technology in Biomedicine
Keywords
Field
DocType
machine learning,kernel function,complex system,artificial immune system,sonogram,nonlinear dynamics,algorithms,classification,immune system,kernel functions,biomedical engineering,kernel,artificial intelligence,artificial immune systems
Kernel (linear algebra),Complex system,Small number,Artificial immune system,Nonlinear system,Computer science,Nonlinear classification,Artificial intelligence,Clonal selection,Machine learning,Kernel (statistics)
Journal
Volume
Issue
ISSN
13
4
1089-7771
Citations 
PageRank 
References 
5
0.40
10
Authors
5
Name
Order
Citations
PageRank
Seral Özsen1877.32
Salih Güneş2126778.53
Sadık Kara3768.83
Fatma Latifoğlu4677.16
LatifoǧluFatma550.40