An ensemble kernel classifier with immune clonal selection algorithm for automatic discriminant of primary open-angle glaucoma

An ensemble kernel classifier is proposed in this paper by integrating a kernel principal component analysis (KPCA) with a support vector machine (SVM) as well as an immune clonal selection algorithm (ICSA). The KPCA approach is used to extract features, whereas the SVM technique is employed to deal with classification, and the ICSA is applied to optimize the parameters of the proposed scheme. The proposed ensemble classifier can automatically select the kernel type and optimize its parameter sets, in order to produce various SVM classifiers with different kernels. Regardless of whether the data is linear or nonlinear, an optimum classification result can be obtained. In order to demonstrate the effectiveness of the classifier, it is applied to discriminate the primary open-angle glaucoma (POAG) using a standard classification dataset. Experimental results reveal that the proposed ensemble classifier is accurate and more effective when compared to other approaches in the literature. It is envisaged that ensemble kernel classifier could hold a high potential in classification of pattern recognition problems.