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Learning Continuous Time Bayesian Network Classifiers Using MapReduce

机译:使用MapReduce学习连续时间贝叶斯网络分类器

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Parameter and structural learning on continuous time Bayesian network classifiers are challenging tasks when you are dealing with big data. This paper describes an efficient scalable parallel algorithm for parameter and structural learning in the case of complete data using the MapReduce framework. Two popular instances of classifiers are analyzed, namely the continuous time naive Bayes and the continuous time tree augmented naive Bayes. Details of the proposed algorithm are presented using Hadoop, an open-source implementation of a distributed file system and the MapReduce framework for distributed data processing. Performance evaluation of the designed algorithm shows a robust parallel scaling.
机译:当您处理大数据时,连续时间上的参数和结构学习贝叶斯网络分类器是具有挑战性的任务。本文介绍了使用MapReduce框架在完整数据的情况下用于参数和结构学习的高效可伸缩并行算法。分析了两个流行的分类器实例,即连续时间朴素贝叶斯和连续时间树增强朴素贝叶斯。使用Hadoop,分布式文件系统的开源实现以及用于分布式数据处理的MapReduce框架,展示了所提出算法的细节。对设计算法的性能评估显示了鲁棒的并行缩放。

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