Multiple Classifiers Combination Model Based on Particle Swarm Optimization
Aiming at improving the classification performance,a combination model of multiple classifier systems is presented,which takes the Sum rule and majority voting as its special cases.Particle Swarm Optimization(PSO)is used to select parameters for the model.An experimental investigation is performed on the UCI datasets and the encouraging results are obtained.The PSO combination model proposed in this paper is better than the other fusion methods given in the larger datasets with 91.44%,53.19%,5.76% and 2.03% reduction in error rate on 4 datasets,respectively.It is also shown that the rejection of weak classifier in the ensemble can improve classification performance further.