多核贝叶斯优化的模型决策树算法

构造模型决策树时超参数较多,参数组合复杂,利用网格搜索等调参方法将会消耗大量的时间,影响模型性能的提升。提出了一种多核贝叶斯优化的模型决策树算法,该算法为应对不同分类数据特性,采用三种高斯过程建模寻优,利用贝叶斯优化技术,选出最优的参数组合。实验结果表明,所提算法在参数寻优上要优于传统的模型决策树寻优方法,并且能够在迭代次数不多的情况下找到全局最优参数值,在一定程度上提升了算法的分类性能,节省了大量的调参时间。

Algorithm for model decision tree with multi-kernel Bayesian optimization

In the construction of the model decision tree, there are many parameters and the parameter combination is complex. The use of grid search and other parameter tuning methods will consume a lot of time, which will affect the improvement of the model performance. A model decision tree with multi-kernel bayesian optimization was proposed. In order to deal with the characteristics of different classified data, three Gaussian processes were used for modeling optimization. The Bayesian optimization technique was used to select the best parameter combination. The experimental results show that the proposed algorithm is better than the traditional model decision tree method in parameter optimization, and can find the global optimal parameter value in the case of a few iterations. To a certain extent, it improves the classification performance of the algorithm and saves a lot of parameter adjustment time.