Multi-class wavelet SVM classifiers using quantum-inspired evolutionary algorithm

Based on quantum-inspired evolutionary algorithm (QEA), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. Quantum-inspired evolutionary optimazition can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM model for the multi-class classifiers is built. And then, classification is studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the approach for the multi-class LS-WSVM classifiers is effective, that can obtain the optimal parameters of LS-WSVM with global searching QEA, and improved LS-WSVM provides excellent precision for classification.