Research on the ensemble learning classification algorithm based on the novel feature selection method

In this paper, a ensemble learning classification algorithm based on the novel feature selection method is proposed. The feature selection method takes full account of the discrimination and class information of each feature by calculating the scores. Specially, the scores are fused for getting a weight for each feature. We select the significant features according to the weights. The result of feature selection will help to improve the classification accuracy. The ensemble learning method improves the classification performance of single classifier. We compare our method with several classical feature selection methods by theoretical analysis and extensive experiments. Experimental results show that our method can achieve higher predictive accuracy than several classical feature selection methods.