A Genetic Algorithms-Based LSSVM Classifier for Fixed-Size Set of Support Vectors

Least Square Support Vector Machines (LSSVMs) are an alternative to SVMs because the training process of LSSVM classifiers only requires to solve a linear equation system instead of solving a quadratic programming optimization problem. Nevertheless, the absence of sparseness in the solution (i.e. the Lagrange multipliers vector) obtained is a significant drawback which must be overcome. This work presents a new approach to building Sparse Least Square Support Vector Machines with fixed-size of support vectors for classification tasks. Our proposal named FSGAS-LSSVM relies on a binary-encoding single-objective genetic algorithms, in which the standard reproduction and mutation operators must be modified. The main idea is to leave a few support vectors out of the solution without affecting the classifier’s accuracy and even improving it. In our proposal, GAs are used to select a suitable fixed-size set of support vectors by removing non-relevant patterns or those ones, which can be corrupted with noise and thus prevent classifiers to achieve higher accuracies.