Wavelet SVM ensemble for pattern classification with quantum-inspired evolutionary algorithm

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