基于Catboost的特征选择算法

A feature selection algorithm based on Catboost

应用Catboost构建树模型的分割指标作为特征选择度量标准,在原始前向搜索策略的基础上,结合两种度量标准计算综合加权值进行特征搜索。在UCI数据库中选择7个不同维度的标准数据集进行了测试,并与其他6种算法进行了对比分析。

The metrics for building tree in Catboost are taken as the standards of feature selection.The combined metrics based on the original forward search strategy are used to calculate the weighted value for searching the features.7 different dimensional standard data sets selected from the UCI database are tested,and the algorithm is compared with other 6 different methods.