bagging是一种用来提高学习算法准确度的方法,这种方法通过构造一个预测函数系列,然后以一定的方式将它们组合成一个预测函数。Bagging要求“不稳定”(不稳定是指数据集的小的变动能够使得分类结果的显著的变动)的分类方法。比如:决策树,神经网络算法。

最新论文

Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a significantly growing number of applications, the underlying machine learning models are often chosen in an ad-hoc manner. We show in this work that three classical regression methods are able to achieve meaningful results for ELA-based algorithm selection. For those three models -- random forests, decision trees, and bagging decision trees -- the quality of the regression models is highly impacted by the chosen hyper-parameters. This has significant effects also on the quality of the algorithm selectors that are built on top of these regressions. By comparing a total number of 30 different models, each coupled with 2 complementary regression strategies, we derive guidelines for the tuning of the regression models and provide general recommendations for a more systematic use of classical machine learning models in landscape-aware algorithm selection. We point out that a choice of the machine learning model merits to be carefully undertaken and further investigated.

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