Unsupervised Evolutionary Algorithm for Dynamic Bayesian Network Structure Learning

机译：动态贝叶斯网络结构学习的无监督进化算法

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摘要

The introduction of temporal dimension makes it difficult and complex to learn dynamic Bayesian network (DBN) structure for huge search space, hence many studies focus on some particular types of DBN, such as dynamic Naive Bayesian Classifier (DNBC). In order to overcome the limited applicability of DBN structure learning methods, this paper proposes an unsupervised evolutionary algorithm in which the selection of initial population has been implemented by means of mutual information to reduce the search space. Furthermore, in view of the poor performance of traditional encoding scheme and the recount of Bayesian information criterion (BIC) score when calculating the individual fitness, we provide a new structure representation without a necessity of the acyclicity test and an updating algorithm for BIC scores with the help of family inheritance to improve the efficiency. Simulations on synthetic data demonstrate that the proposed unsupervised evolutionary algorithm is effective in DBN structure learning.

机译：时间维的引入使得难以为庞大的搜索空间学习动态贝叶斯网络（DBN）结构，因此许多研究集中在某些特定类型的DBN上，例如动态朴素贝叶斯分类器（DNBC）。为了克服DBN结构学习方法的局限性，本文提出了一种无监督进化算法，该算法通过互信息实现了初始种群的选择，以减少搜索空间。此外，鉴于传统编码方案的性能较差以及计算个人适应度时的贝叶斯信息准则（BIC）分数的重新计数，我们提供了无需进行非循环性测试的新结构表示形式，以及针对BIC分数的更新算法。借助家庭继承来提高效率。对合成数据的仿真表明，所提出的无监督进化算法在DBN结构学习中是有效的。

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来源
《Advanced methodologies for bayesian networks》|2015年|136-151|共16页
会议地点 Yokohama(JP)
作者
Jingguo Dai; Jia Ren;
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作者单位

College of Information Science and Technology, Hainan University, Haikou 570228, China;

College of Information Science and Technology, Hainan University, Haikou 570228, China;

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会议组织
原文格式 PDF
正文语种 eng
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关键词
Dynamic Bayesian networks; Structure learning; Genetic algorithm; Mutual information; Family BIC score;

机译：动态贝叶斯网络；结构学习；遗传算法相互信息；家庭BIC分数;

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Unsupervised Evolutionary Algorithm for Dynamic Bayesian Network Structure Learning

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