随机和贝叶斯决策树集成分类不确定性的实验比较
Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles
摘要：
在本文中，我们实验比较的分类不确定性的随机决策树（DT）集成技术和贝叶斯DT技术与重新启动策略上的合成数据集，以及在机器学习社区常用的一些数据集。对于分类不确定性的定量评估，我们使用不确定性包络处理类后验分布和给定的置信概率。计数的分类器的结果，这种技术产生的分类不确定性的可行的评估。在我们的实验中使用这种技术，我们发现贝叶斯DT技术优于随机DT集成技术。
英文摘要：
In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique.
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