發(fā)布時間：2018-05-03 12:11

  本文選題：貝葉斯網(wǎng)絡 + 結(jié)構(gòu)學習�。� 參考：《太原理工大學》2017年碩士論文

【摘要】：貝葉斯網(wǎng)絡分類器是具有很強的學習和推理能力,是數(shù)據(jù)處理領域研究熱點之一。雖然貝葉斯網(wǎng)絡分類器表現(xiàn)出了良好的分類預測性能,但是仍存在先驗只是利用率不高、實用性差而導致學習不能得到最優(yōu)網(wǎng)絡結(jié)構(gòu),從而影響了分類器的性能。如何更好的實現(xiàn)現(xiàn)有貝葉斯網(wǎng)絡分類器的并行化仍然是亟待解決的問題之一。為了解決上述問題,本文開展了并行貝葉斯網(wǎng)絡分類器相關的研究,設計并實現(xiàn)了新型的并行貝葉斯網(wǎng)絡分類器,主要包括以下內(nèi)容:(1)本文在馬氏鏈蒙特卡洛算法(Markov Chain Monte Carlo,MCMC)的基礎上引入存在、缺失和PD/CPD三種先驗知識,提出了一種新的貝葉斯網(wǎng)絡結(jié)構(gòu)學習算法C-MCMC(Constrained-MCMC),運用以及先驗知識對MCMC貝葉斯網(wǎng)絡結(jié)構(gòu)學習算法的影響,并通過一系列的實驗驗證了算法的有效性,從而學習得到更加優(yōu)良的貝葉斯網(wǎng)絡;(2)將C-MCMC貝葉斯網(wǎng)絡結(jié)構(gòu)學習算法應用在傳統(tǒng)的增廣樸素貝葉斯分類器(BAN)和通用貝葉斯網(wǎng)絡分類器(GBN)中,并進行相應的參數(shù)估計,從而設計了C-MCMC BAN分類器和C-MCMC GBN分類器;借助開源平臺Hadoop的并行編程模型MapReduce,設計了相應的Map函數(shù)與Reduce函數(shù),對C-MCMC貝葉斯網(wǎng)絡分類器使用MapReduce并行編程框架進行了并行化,給出了具體的編程實現(xiàn)過程,并通過搭建Hadoop平臺驗證了算法并行化對算法效率的改進和提高。實驗結(jié)果表明,本文所設計的貝葉斯網(wǎng)絡分類器的性能優(yōu)于傳統(tǒng)的貝葉斯網(wǎng)絡分類器,有著較高的分類準確率和效率,且適用于大數(shù)據(jù)處理的場合,可以被應用于多個場合,具有廣闊的市場應用前景。
[Abstract]:Bayesian network classifier has strong learning and reasoning ability, and it is one of the research hotspots in data processing field. Although Bayesian network classifier has shown good classification and prediction performance, there is still a priori only low utilization ratio and poor practicability, which leads to the failure of learning to obtain the optimal network structure, thus affecting the performance of classifier. How to better realize the parallelization of existing Bayesian network classifiers is still one of the problems to be solved. In order to solve the above problems, this paper develops the research of parallel Bayesian network classifier, designs and implements a new parallel Bayesian network classifier. The main contents are as follows: 1) this paper introduces three kinds of prior knowledge of existence, missing and PD/CPD on the basis of Markov Chain Monte Monte MCMCs of Markov chain Monte Carlo algorithm. In this paper, a new Bayesian network structure learning algorithm, C-MCMC- Constrained-MCMC-, is proposed. The effect of using and prior knowledge on the learning algorithm of MCMC Bayesian network structure is proved by a series of experiments, and the effectiveness of the algorithm is verified by a series of experiments. Thus, a better Bayesian network is obtained. The C-MCMC Bayesian network structure learning algorithm is applied to the traditional augmented naive Bayesian classifier (Ann) and the general Bayesian network classifier (GBN), and the corresponding parameters are estimated. In this paper, C-MCMC BAN classifier and C-MCMC GBN classifier are designed, the corresponding Map function and Reduce function are designed with the help of Hadoop parallel programming model of open source platform, and C-MCMC Bayesian network classifier is parallelized using MapReduce parallel programming framework. The implementation process of the algorithm is given, and the improvement and improvement of the algorithm efficiency are verified by building the Hadoop platform. The experimental results show that the proposed Bayesian network classifier is superior to the traditional Bayesian network classifier and has high classification accuracy and efficiency. It is suitable for big data processing and can be applied to many occasions. Has broad market application prospect.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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