不确定性包络技术中贝叶斯和随机决策树集成的比较
Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique
摘要：
多分类器系统（MCS）允许评估分类结果的不确定性，这对于安全关键应用至关重要。分类的不确定性由可用于训练的数据量、分类器多样性和所需性能之间的权衡来确定。MCS的可解释性也可以为负责进行可靠分类的专家提供有用的信息。基于这个原因，决策树（DT）似乎是对专家有吸引力的分类模型。利用这种分类模型的MCS所需的多样性可以通过使用两种技术来实现，贝叶斯模型平均和随机DT合奏。这两种技术在应用于现实世界的问题时都显示了有希望的结果。在本文中，我们实验比较了贝叶斯模型平均与重新启动策略和随机DT集成的分类不确定性的合成数据集和一些领域的问题，通常用于机器学习社区。为了使贝叶斯DT平均可行，我们使用马尔可夫链蒙特卡罗技术。分类的不确定性内的不确定性包络技术处理类后验分布和给定的置信概率进行评估。探索完整的后验分布，这种技术产生了现实的估计，可以很容易地解释统计术语。在我们的实验中，我们发现，贝叶斯DT优于随机DT合奏内的不确定性包络技术。
英文摘要：
Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required performance. The interpretability of MCSs can also give useful information for experts responsible for making reliable classifications. For this reason Decision Trees (DTs) seem to be attractive classification models for experts. The required diversity of MCSs exploiting such classification models can be achieved by using two techniques, the Bayesian model averaging and the randomised DT ensemble. Both techniques have revealed promising results when applied to real-world problems. In this paper we experimentally compare the classification uncertainty of the Bayesian model averaging with a restarting strategy and the randomised DT ensemble on a synthetic dataset and some domain problems commonly used in the machine learning community. To make the Bayesian DT averaging feasible, we use a Markov Chain Monte Carlo technique. The classification uncertainty is evaluated within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. Exploring a full posterior distribution, this technique produces realistic estimates which can be easily interpreted in statistical terms. In our experiments we found out that the Bayesian DTs are superior to the randomised DT ensembles within the Uncertainty Envelope technique.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
