论文标题
使用双极论证和马尔可夫网络解释随机森林（技术报告）
Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)
论文作者
论文摘要
随机森林是决策树的合奏，可用于解决各种机器学习问题。但是，由于树木的数量及其个体规模可能很大，因此他们的决策过程通常是无法理解的。为了推理决策过程，我们建议将其表示为论证问题。我们使用Markov网络编码来概括足够和必要的论点解释，讨论这些解释的相关性，并与文献中绑架性解释的家庭建立关系。由于解释问题的复杂性很高，我们讨论了概率近似算法并提出了第一个实验结果。
Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large, their decision making process is often incomprehensible. In order to reason about the decision process, we propose representing it as an argumentation problem. We generalize sufficient and necessary argumentative explanations using a Markov network encoding, discuss the relevance of these explanations and establish relationships to families of abductive explanations from the literature. As the complexity of the explanation problems is high, we discuss a probabilistic approximation algorithm and present first experimental results.
