Transfer learning for Bayesian networks

迁移学习解决了如何通过利用来自另一个不同但相关的域（源域）的现有模型来学习域（目标域）的模型的问题。关键问题是如何使源领域中已有的知识适应目标领域。因此，领域知识的表示起着重要的作用。贝叶斯网络（BN）是领域知识的显式图形表示。对源域的BN（源BN）执行操作以获得目标域的新BN（目标BN）是自然的。因此，BN适合于迁移学习的场景。另一方面，尽管BN已经被深入研究了几十年，但学习BN仍然具有相当大的挑战性，特别是在训练数据稀缺的情况下。迁移学习方法可以提供一种有效的方法来克服这个问题在学习BN。 本研究将系统地研究贝叶斯网络的迁移学习方法。我们将首先研究现有的迁移学习方法的适用性学习BN的任务，如实例转移，特征表示转移，参数转移等，然后新的迁移学习方法，适合学习BN的任务将被研究和开发。此外，有效的迁移学习算法的BN，包括结构学习和参数学习，将进行研究。在这项研究中的学习算法的评估是基于常用的基准数据集上的实验。 在这一领域的现有研究处于早期阶段。本研究的新奇和意义体现在以下几个方面：（1）.在迁移学习的场景中，BN没有得到很好的注意。拟议的研究将证明BN是一种有效的迁移学习方法。（二）、结构学习仍然是一个具有挑战性的问题，在学习BN。本研究将为缓解结构学习问题提供一种新的途径，从而开发出更有效的BN学习算法。（三）、这项研究有望产生有效的和高效的新方法和算法，为现实世界的数据挖掘应用。

Transfer learning addresses the problem of how to learn a model for a domain (target domain) by utilizing an existing model from another different but related domain (source domain). The key issue is how to adapt the existing knowledge in source domain to fit target domain. Thus the representation of domain knowledge plays an important role. A Bayesian network (BN) is an explicit graphical representation of domain knowledge. It is natural to perform manipulations on a BN for source domain (source BN) to obtain a new BN for target domain (target BN). Therefore, BNs are suitable in the scenario of transfer learning. On the other hand, although BNs have been intensively studied for decades, learning BNs still remains quite challenging, especially when training data is scarce. The transfer learning approach can provide an effective way to overcome this issue in learning BNs. This proposed research will systematically study the transfer learning methods for BNs. We will first examine the suitability of the existing transfer learning approaches for the task of learning BNs, such as instance transfer, feature representation transfer, parameter transfer, etc. Then novel transfer learning methods that fit the task of learning BNs will be studied and developed. In addition, effective transfer learning algorithms for BNs, including both structure learning and parameter learning, will be studied. The evaluation of learning algorithms in this research is based on experiments on the commonly used benchmark data sets. The existing research in this field is in an early stage. The novelty and significance of this research are reflected in the following aspects: (1). BNs have not been well noticed in the scenario of transfer learning. The proposed research will demonstrate BNs as an effective transfer learning approach. (2). Structure learning is still a challenging issue in learning BNs. This research will provide a new approach to alleviate the structure learning issue, and thus develop more effective and efficient BN learning algorithms. (3). This research is expected to produce effective and efficient novel methods and algorithms for real-world data mining applications.