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

机译：基于新特征选择方法的集成学习分类算法研究

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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.
机译：提出了一种基于特征选择方法的集成学习分类算法。特征选择方法通过计算分数来充分考虑每个特征的区别和类别信息。特别地，分数被融合以获得每个特征的权重。我们根据权重选择重要特征。特征选择的结果将有助于提高分类精度。集成学习方法提高了单个分类器的分类性能。通过理论分析和广泛的实验，我们将我们的方法与几种经典的特征选择方法进行了比较。实验结果表明，与几种经典特征选择方法相比，我们的方法可以实现更高的预测精度。

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