【摘要】 针对特征选择过程中特征评价指标单一性的问题,基于集成学习中的极端梯度提升算法,提出一种新的特征选择算法.该算法首先应用极端梯度提升算法中构建集成树模型的指标作为特征选择的特征重要性度量指标,然后利用一种新的双向搜索策略,权衡了多种特征重要性对结果的影响,并优化了评价过程的效率.通过11个不同维度的标准数据集进行测试,实验结果表明,该算法能增加特征子集的多样性,加快特征选择的速度,并在中维和低维数据集上均具有较高的计算效率,且能处理高维数据集.更多还原

【Abstract】 Aiming at the problem of single feature evaluation criteria in feature selection process, we proposed a new feature selection algorithm based on the extreme gradient boosting algorithm in ensemble learning. Firstly, the metrics of building ensemble tree model in the extreme gradient boosting algorithm were used as the importance measures of features in feature selection, and then a new bidirectional search strategy was used to balance the influence of multiple feature importance on the results, and optimize the efficiency of evaluation process. Through the test of 11 different dimensions of standard datasets, the experimental results show that the algorithm can increase the diversity of feature subsets, accelerate the speed of feature selection, and has high computational efficiency on both medium and low dimensional datasets, and can deal with high-dimensional datasets.更多还原