Explanatory Analysis of Probabilistic Graphical Models based on Discriminative Pattern Mining

在这个项目中，我们开发了一种概率模型的解释性分析方法，特别是贝叶斯网络，为机器学习技术带来可解释性。 我们的分析旨在从大量可能的观测结果中找到一个合适的解释。 为了在实践中做到这一点，我们建立了一些复杂的技术，判别模式挖掘的基础上流行的频繁模式挖掘算法称为FP-Growth。 最后，我们改进了解释的选择标准，并得到了一个快速的判别模式挖掘算法。 虽然还有一个未来的工作，优化概率推理，我们的解释性分析，我们已经获得了一些新的见解和原型工具，实现我们的解释性分析方法。

In this project, we have developed an explanatory analysis method for probabilistic models, Bayesian networks in paticular, to bring explanability to machine learning techniques. Our analysis aims to find an appropriate explanation for the observation from a huge number of possible ones. To do this in practical time, we built some sophisticated techniques for discriminative pattern mining based on a popular frequent pattern mining algorithm called FP-Growth. Finally, we have achieved to refine the selection criteria of explanations and to have a fast discriminative pattern mining algorithm. Although there remains a future work on optimizing probabilistic inference for our explanatory analysis, we have obtained a couple of new insights and prototype tools towards an implementation of our explanatory analysis method.