Research On Support Vector Machine Ensemble Learning Algorithm

Ensemble learning can greatly improve the generalization ability of learning system by training multiple base learners and combining their results, which has been the forth hot investigation orientation of machine learning.It also provides another way to improve the generalization ability of machine learning. Support vector machine, as a "stable" learning algorithm, is a challenge to ensemble learning. It is a hot research point to investigate and quest for new-style support vector machine ensemble learning algorithm. The research of support vector machine ensemble starts lately, and has few research productions. It is the key point of the support vector machine ensemble learning algorithm about how to work out much more effective ensemble learning algorithms. The paper focuses on the view point of individual production and conclusion combination to fully mining the predominance and potentiality of support vector machine ensemble.According to the sensitivity of support vector machine to the type of kernel function and model parameters. It aims at that the existed parameter manipulating methods did not consider the influence of the type of kernel function to the performance of support vector machine. The flexible hybrid kernel function is introduced and the involved parameters are manipulated. Actually a support vector machine ensemble algorithm based on model manipulating is proposed. The simulated experiments results show that the diversity and generalization performance is improved by introducing much manipulated parameters.The research of support vector machine ensemble algorithm is based on model and feature double disturbance mechanism. The feature disturbance is introduced into model disturbance. The existed feature manipulating methods did not consider the influence of the feature relativity to the performance of support vector machine and the diversity of ensemble.The feature transformation is introduced, the ICA is used to transform the feature space to take out the feature relativity, the support vector machine ensemble algorithm based on model and feature double disturbance mechanism is proposed. The simulated experiment results show that the diversity and generalization performance is improved further.Selective ensemble method selects partial individuals from ensemble system to improve generalization ability. The classical selective ensemble methods have the disadvantage of higher computation complexity, lower learning efficiency, and lower performance.A selective support vector machine ensemble algorithm based on artificial fish swarm algorithm, which has the virtue of overall solution, not sensitive to initial value, tough, rapidly converge, is proposed to optimize the combined weight. The simulated experiment results show that it can improve generalization ability and learning efficiency, decrease the scale of ensemble.Support vector machine ensemble based on fuzzy integral method can make full use of the measurement level information of support vector machine to improve generalization performance of support vector machine ensemble. The existed fuzzy integral fusion methods used the prior information of training samples to determine the value of fuzzy density, which is the same to any samples and can not reflect the different importance of support vector machine to different samples. A support vector machine ensemble based on adaptive fuzzy integral is presented, the classification confidence of individual support vector machine to test sample is determined according to the measurement level information and the adaptive fuzzy density is determined according to classification confidence. The simulated experiment results show that the proposed method can improve the performance further.