Kernel k-nearest neighbor classifier based on decision tree ensemble for SAR modeling analysis
Kernel approaches that can effectively solve nonlinear problems using implicit nonlinear mapping have been gaining popularity in the field of chemistry. In the present study, a novel tree kernel k-nearest neighbor algorithm (TKk-NN) has been proposed. First, an informative novel tree kernel is constructed based on the decision tree ensemble. The constructed tree kernel can effectively use important variables for classification and neglect useless variables through variable importance ranking during the process of building the kernel. Under the framework of kernel methods, this tree kernel is then extended to the k-nearest neighbor algorithm. Three SAR datasets together with the simulated data were used to test the performance of k-NN with tree and radial basis function kernels. The results show that TKk-NN really is an attractive alternative technique.