A hybrid approach for sparse Least Squares Support Vector Machines

We present in this work a hybrid strategy for training Least Squares Support Vector Machines (LS-SVMs), in order to eliminate their greatest drawback when comparing to original Support Vector Machines (SVMs), the inexistence of support vectors' automatic detection, the so called loss of sparseness. The main characteristic of LS-SVMs is the low computational complexity comparing to SVMs, without quality loss in the solution, because the principles that both have been based are the same. In this work, we use a sample selection technique called Reduced Remaining Subset (RRS), which is based on a modified nearest neighbor rule, in order to choose the best samples to represent each class. After that, LS-SVMs use the selected samples as support vectors to find the decision surface between the classes. Some experiments are presented to compare the proposed approach with two existent methods that also aim to impose sparseness in LS-SVMs.