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| 基于自编码器的贝叶斯网嵌入及概率推理 |
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杜斯1,2, 祁志卫1,2, 岳昆1,2, 段亮1,2, 王笳辉1,2
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1.云南大学 信息学院, 云南 昆明 650500;2.云南省智能系统与计算重点实验室 (云南大学), 云南 昆明 650500
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| 摘要: |
| 贝叶斯网(BN)是不确定性知识表示和推理的基本框架, 广泛用于社交网络、知识图谱和医疗诊断等领域. 特定领域中基于BN的分析诊断和决策支持, 其核心计算任务是基于BN进行多次概率推理. 然而, 使用传统的概率推理方法, 基于同一BN的多次概率推理其中间过程存在很多重复的计算结果, 具有较高的时间复杂度. 为了提高多次概率推理的效率, 提出易于重用和易于计算的贝叶斯网嵌入及相应的概率推理方法. 首先, 借鉴图嵌入的基本思想, 使用点互信息矩阵来表示BN的有向无环图结构和条件概率参数, 提出基于自编码器和注意力机制的BN嵌入方法. 其中, 自编码器的每一编码层利用节点与其邻居节点(父节点和子节点)的相关性生成节点嵌入, 从而在嵌入向量中保存BN节点间的概率依赖关系. 然后, 使用嵌入向量之间的距离来度量节点之间的联合概率, 提出基于嵌入向量的BN概率推理方法. 实验证明, 针对BN的多次概率推理, 所提方法的效率高于现有方法, 且能得到准确的推理结果. |
| 关键词: 贝叶斯网 高效概率推理 图嵌入 自编码器 注意力机制 |
| DOI：10.13328/j.cnki.jos.006670 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62002311);云南省基础研究计划杰出青年项目(2019FJ011);云南省重大科技专项(202002AD080002);云南省基础研究项目(202001BB050052) |
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| Autoencoder-based Bayesian Network Embedding and Probabilistic Inferences |
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DU Si1,2, QI Zhi-Wei1,2, YUE Kun1,2, DUAN Liang1,2, WANG Jia-Hui1,2
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1.School of Information Science and Engineering, Yunnan University, Kunming 650500, China;2.Yunnan Key Laboratory of Intelligent Systems and Computing (Yunnan University), Kunming 650500, China
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| Abstract: |
| Bayesian network (BN), as a preliminary framework for representing and inferring uncertain knowledge, is widely used in social network, knowledge graph, medical diagnosis, etc. The centric computing task of BN-based analysis, diagnosis, and decision-support in specific fields includes multiple probabilistic inferences. However, the high time complexity is doomed on the same BN by using the traditional inference methods, due to the several intermediate results of probability calculations that cannot be shared and reused among different inferences. Therefore, to improve the overall efficiency of multiple inferences on the same BN, this study proposes the method of BN embedding and corresponding probabilistic inferences. First, by incorporating the idea of graph embedding, the study proposes a BN embedding method based on the autoencoder and attention mechanism by transforming BN into the point mutual information matrix to preserve the directed a cyclic graph and conditional probability parameters simultaneously. Specifically, each coding layer of the autoencoder generates node embedding by using the correlation between a node and its neighbors (parent and child nodes) to preserve the probabilistic dependencies. Then, the method for probabilistic inferences to measure the joint probability by using the distance between embedding vectors is proposed. Experimental results show that the proposed method outperforms other state-of-the-art methods in efficiency, achieving accurate results of probabilistic inferences. |
| Key words: Bayesian network (BN) efficient probabilistic inference graph embedding autoencoder attention mechanism |
