Visible to the public A Method for Hybrid Bayesian Network Structure Learning from Massive Data Using MapReduce

TitleA Method for Hybrid Bayesian Network Structure Learning from Massive Data Using MapReduce
Publication TypeConference Paper
Year of Publication2017
AuthorsLi, S., Wang, B.
Conference Name2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids)
KeywordsAlgorithm design and analysis, Bayes methods, Bayesian Network, constraint-based algorithm, data mining, data mining model, Data models, directed graphs, distributed hybrid structure learning algorithm, hybrid Bayesian network structure learning, Hybrid Learning, Information Reuse, knowledge representation, learning (artificial intelligence), MapReduce, Markov processes, massive data, Mutual information, network theory (graphs), parallel processing, pubcrawl, Resiliency, score-and-search-based algorithm, search problems, security, Structure Learning, styling, uncertain knowledge representation
AbstractBayesian Network is the popular and important data mining model for representing uncertain knowledge. For large scale data it is often too costly to learn the accurate structure. To resolve this problem, much work has been done on migrating the structure learning algorithms to the MapReduce framework. In this paper, we introduce a distributed hybrid structure learning algorithm by combining the advantages of constraint-based and score-and-search-based algorithms. By reusing the intermediate results of MapReduce, the algorithm greatly simplified the computing work and got good results in both efficiency and accuracy.
Citation Keyli_method_2017