發(fā)布時間：2018-06-18 22:59

  本文選題：貝葉斯網(wǎng)絡 + 混合結構學習��； 參考：《山西財經(jīng)大學》2017年碩士論文

【摘要】：近年來,貝葉斯網(wǎng)絡(Bayesian network,BN)在不確定性知識表示與概率推理方面發(fā)揮著越來越重要的作用。作為一種重要的圖模型方法,該模型已被廣泛應用于生物信息學、金融預測分析、編碼學、數(shù)據(jù)挖掘與機器學習等領域。其中,BN結構學習是BN推理研究中的重要問題,也是該模型推向應用的前提基礎。然而,當前較為流行的兩階段混合結構學習算法中,大多存在兩個問題:第一階段無向超結構學習中存在容易丟失弱關系的邊的問題;第二階段的爬山隨機搜索時存在易陷入局部極值的問題。針對這些問題,本文基于超結構和隨機搜索策略,研究了兩種BN結構的混合學習算法。具體研究內(nèi)容和創(chuàng)新之處包括:第一,提出了基于超結構和隨機搜索的SSRS算法。首先采用Opt01ss算法學習超結構,盡可能地避免出現(xiàn)丟邊現(xiàn)象。然后,給出基于超結構的兩種隨機搜索操作,分析初始網(wǎng)絡的隨機產(chǎn)生規(guī)則和對初始網(wǎng)絡的隨機優(yōu)化策略,重點提出SSRS結構學習算法,該算法在一定程度上可以很好地跳出局部最優(yōu)極值。第二,提出了擴展的SSRS算法,即E-SSRS算法。在E-SSRS算法中,首先在初始網(wǎng)絡的選擇階段,增加了通過評分選擇對每條邊添加方向之步驟,使得選取的初始網(wǎng)絡更靠近最優(yōu)網(wǎng)絡。然后,在優(yōu)化階段,對刪邊策略進行了擴展,使用了基于馬爾科夫毯的策略對網(wǎng)絡進行修剪,進一步提出E-SSRS算法。通過擴展,使該算法減少了搜索次數(shù),進一步提高算法效率。第三,設計并實現(xiàn)了SSRS算法和E-SSRS算法。分別在標準Survey,Asia和Sachs,Child網(wǎng)絡上,通過幾種評價指標,并與已有六種混合學習算法實驗結果的對比分析,驗證了本文所提出的兩種混合結構學習算法的良好性能。
[Abstract]:In recent years, Bayesian Network (BN) plays an increasingly important role in uncertain knowledge representation and probabilistic reasoning. As an important graph model method, the model has been widely used in the fields of bioinformatics, financial prediction and analysis, coding, data mining and machine learning. The learning of BN structure is an important problem in the research of BN reasoning, and it is also the prerequisite for the application of the model. However, most of the popular two-stage hybrid structure learning algorithms have two problems: the first stage undirected superstructure learning has the problem of losing the edge of weak relation easily in the first stage undirected superstructure learning; In the second stage of the mountain climbing random search, the problem of local extremum is easy to fall into. In order to solve these problems, the hybrid learning algorithms of two BN structures are studied based on hyperstructure and random search strategy. The main contents and innovations are as follows: first, a new SSRS algorithm based on hyperstructure and random search is proposed. First, we use Opt01ss algorithm to learn the superstructure to avoid the phenomenon of edge loss as far as possible. Then, two kinds of random search operations based on superstructure are given, the random generation rules of initial network and the random optimization strategy of initial network are analyzed, and the SSRS structure learning algorithm is put forward. To some extent, the algorithm can get rid of the local optimal extremum. Secondly, an extended SSRS algorithm, E-SSRS algorithm, is proposed. In the E-SSRS algorithm, in the selection stage of the initial network, the step of adding direction to each edge is added to make the selected initial network closer to the optimal network. Then, in the optimization phase, the edge deletion strategy is extended and the network is pruned based on Markov blanket, and an E-SSRS algorithm is proposed. By extending the algorithm, the search times are reduced and the efficiency of the algorithm is further improved. Thirdly, SSRS algorithm and E-SSRS algorithm are designed and implemented. In the standard survey Asia and Sachschild networks, the performance of the two hybrid learning algorithms proposed in this paper is verified by comparing the experimental results with the experimental results of six hybrid learning algorithms.
【學位授予單位】：山西財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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本文編號：2037154