Ozşen Seral, Güneş Salih, Kara Sadik, Latifoğlu Fatma
Department of Electrical and Electronics Engineering, Selcuk University, Konya 42075, Turkey.
IEEE Trans Inf Technol Biomed. 2009 Jul;13(4):621-8. doi: 10.1109/TITB.2009.2019637. Epub 2009 Apr 14.
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.
由于人工免疫系统（AIS）中只有少数复杂系统能解决非线性问题，因此需要开发非线性AIS方法，使其成为知名的求解方法之一。在本研究中，我们开发了一种基于核的AIS，通过将经典AIS的克隆选择模型中的距离计算转换到核空间来提供非线性结构，以弥补这一不足。所开发系统的应用是在取自加州大学欧文分校机器学习库的Statlog心脏病数据集以及用于诊断动脉粥样硬化疾病的多普勒超声图上进行的。该系统在Statlog心脏病数据集上获得了85.93%的分类准确率，而在多普勒数据集上实现了99.09%的分类成功率。基于这些结果，我们的系统似乎是一种潜在的求解方法，并且可以被视为解决硬非线性分类问题的合适方法。
