A Method For Hybrid Bayesian Network Structure Learning from Massive Data Using MapReduce

被引：2

作者：

Li, Shun ^{[1
]}

Wang, Biao ^{[1
]}

机构：

[1] Univ Int Relat, Sch Informat Sci & Technol, Beijing 100091, Peoples R China

来源：

2017 IEEE 3RD INTERNATIONAL CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY, IEEE 3RD INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, (HPSC) AND 2ND IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS) | 2017年

关键词：

Bayesian Network; Structure Learning; MapReduce; styling; Hybrid Learning; ALGORITHM; PARALLEL;

D O I：

10.1109/BigDataSecurity.2017.42

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Bayesian 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.

引用

页码：272 / 276

页数：5

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