A novel method for combining Bayesian networks, theoretical analysis, and its applications
A novel method for combining Bayesian networks, theoretical analysis, and its applications
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DOI：
10.1016/j.patcog.2013.12.005
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发表时间：
2014-05-01
影响因子：
8
通讯作者：
Liao, Stephen Shaoyi
中科院分区：
文献类型：
--
作者：
Feng, Guang;Zhang, Jia-Dong;Liao, Stephen Shaoyi
有效的知识集成在知识工程和基于知识的机器学习中起着非常重要的作用。贝叶斯网络的组合在知识融合中显示出一种很有前途的技术，而如何组合贝叶斯网络仍然是一个具有挑战性的研究课题。一种有效的BN组合方法不应对底层BN施加任何特定的约束，从而使该方法适用于各种知识工程场景。一般来说，一个好的BN组合方法应该满足三个基本准则，即避免循环、保持BN结构的条件无关性和保持单个BN参数的特征。然而，现有的BNS组合方法中没有一种满足上述所有标准。因此，以往关于BNS组合的研究只有很少的理论贡献和有限的实用价值。在本文中，遵循已有的边界网络组合方法所采用的方法，我们假设存在单个边界网络共享的有助于避免循环的祖先顺序。我们首先设计和开发了一种新的BN合并方法，主要集中在以下两个方面：(1)不对底层BN施加任何特定约束的BN合并的通用方法；(2)确保BN合并的后两个准则得到满足的有效方法。通过形式化分析，我们将该方法与三种经典的BNS组合方法的性质进行了比较，从而论证了所提出的BNS组合方法的独特优势。最后，我们将所提出的方法应用于基于用户隐含偏好的推荐系统、用于预测客户存款认购意愿的银行直销以及用于评估患者乳腺癌风险的疾病诊断系统。(C)2013爱思唯尔有限公司。保留所有权利。
Effective knowledge integration plays a very important role in knowledge engineering and knowledge-based machine learning. The combination of Bayesian networks (BNs) has shown a promising technique in knowledge fusion and the way of combining BNs remains a challenging research topic. An effective method of BNs combination should not impose any particular constraints on the underlying BNs such that the method is applicable to a variety of knowledge engineering scenarios. In general, a sound method of BNs combination should satisfy three fundamental criteria, that is, avoiding cycles, preserving the conditional independencies of BN structures, and preserving the characteristics of individual BN parameters, respectively. However, none of the existing BNs combination method satisfies all the aforementioned criteria. Accordingly, there are only marginal theoretical contributions and limited practical values of previous research on BNs combination. In this paper, following the approach adopted by existing BNs combination methods, we assume that there is an ancestral ordering shared by individual BNs that helps avoid cycles. We first design and develop a novel BNs combination method that focuses on the following two aspects: (1) a generic method for combining BNs that does not impose any particular constraints on the underlying BNs, and (2) an effective approach ensuring that the last two criteria of BNs combination are satisfied. Further through a formal analysis, we compare the properties of the proposed method and that of three classical BNs combination methods, and hence to demonstrate the distinctive advantages of the proposed BNs combination method. Finally, we apply the proposed method in recommender systems for estimating users' ratings based on their implicit preferences, bank direct marketing for predicting clients' willingness of deposit subscription, and disease diagnosis for assessing patients' breast cancer risk. (C) 2013 Elsevier Ltd. All rights reserved.