A Novel Kernelized Classifier Based on the Combination of Partially Global and Local Characteristics.
Riadh Ksantini, Raouf Gharbi · LWA · 2015
The Kernel Support Vector Machine (KSVM) has achieved promising classication performance. However, since it is based only on local information (Support Vectors), it is sensitive to directions with large data spread. On the other hand, Kernel Nonparametric Discrimi- nant Analysis (KNDA) is an improvement over the more general Kernel Fisher Discriminant Analysis (KFD), where the normality assumption from KFD is relaxed. Furthermore, KNDA incorporates the partially global information in the Kernel space, to detect the dominant nor- mal directions to the decision surface, which represent the true data spread. However, KNDA relies on the choice of the -nearest neighbors ( NN 's) on the decision boundary. This paper introduces a novel Combined KSVM and KNDA (CKSVMNDA) model which controls the spread of the data, while maximizing a relative margin separating the data classes. This model is considered as an improvement to KSVM by incorporating the data spread information represented by the dominant normal directions to the decision boundary. This can also be viewed as an extension to the KNDA where the support vectors improve the choice of -nearest neighbors (