MultiBoost with SVM-based ensemble classification method and application
Xian Li · Kongzhi yu juece · 2015
For pattern recognition in the network fault diagnosis, an optimal SVM ensemble learning method based on Multi Boost is proposed. Firstly, the parameters of SVM-base-classifier are optimized by using the adaptive hormone modulation genetic algorithm(HMGA). Then, multi-base-classifiers are integrated by using the Multi Boost algorithm.Finally, with multiple ensemble optimal classifiers as the core, the fault diagnosis system is established. Simulation results show that the proposed method can not only reduce the number of iteration and lower the computing cost, but also improve the fault diagnosis accuracy of the network fault diagnosis system.