本文關(guān)鍵詞：高維因果網(wǎng)與高校資產(chǎn)管理的模糊推理研究　出處：《華南理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 貝葉斯網(wǎng)絡(luò) 因果網(wǎng) 高校資產(chǎn)管理 模糊推理

【摘要】：貝葉斯網(wǎng)絡(luò)作為處理變量間因果關(guān)系表達和推理的有效工具,在人工智能與數(shù)據(jù)挖掘領(lǐng)域受到廣泛的應(yīng)用。但傳統(tǒng)的貝葉斯網(wǎng)絡(luò)也存在以下三個問題,制約進一步的發(fā)展。首先是傳統(tǒng)貝葉斯網(wǎng)絡(luò)只能處理離散變量,處理其它類型變量時需要先進行離散化,這樣容易產(chǎn)生 邊緣銳化‖問題。特別是面對模糊變量時,會造成大量的信息丟失,影響推理精度。然后是貝葉斯網(wǎng)絡(luò)的結(jié)構(gòu)學(xué)習(xí)是NP-HARD問題,在高維數(shù)據(jù)下,其網(wǎng)絡(luò)結(jié)構(gòu)的搜索空間會呈指數(shù)增長,常用結(jié)構(gòu)學(xué)習(xí)算法的效率都會變得低下。最后是傳統(tǒng)的結(jié)構(gòu)學(xué)習(xí)算法無法識別馬爾科夫等價類,當(dāng)搜索空間存在大量的馬爾科夫等價類,搜索效率就會很低,且很難收斂到最優(yōu)解中。針對問題一,本文根據(jù)模糊理論對傳統(tǒng)貝葉斯網(wǎng)絡(luò)進行擴展,給出了能兼容模糊變量的混合貝葉斯網(wǎng)絡(luò)的完整方案。對于問題二,則提出了一種新型的約簡組合方案通過把建網(wǎng)問題分割為多子網(wǎng)的構(gòu)建,降低高維數(shù)據(jù)的影響。同時該方法在聚類的過程同時確定子網(wǎng)間的連接點,避免了二次搜索,與同類算法相比降低了計算復(fù)雜度。聚類的過程使用了基于因果的相似度,降低分網(wǎng)建立對結(jié)構(gòu)質(zhì)量的負面影響。最后針對問題3,則把基于信息幾何的因果推斷與爬山法結(jié)合,提出了改進的爬山算法IGCI-HC,解決傳統(tǒng)結(jié)構(gòu)學(xué)習(xí)算法無法識別馬爾可夫等價類的不足。高校資產(chǎn)管理直接影響高校的建設(shè)與發(fā)展。影響高校資產(chǎn)管理的因素有多個,具有高維且涉及因素類型多的特點,如何挖掘分析因素間以及資產(chǎn)管理效率間的因果關(guān)系并給予知識推理決策輔助,是本文的應(yīng)用要點。本文根據(jù)72所高校的資產(chǎn)管理相關(guān)數(shù)據(jù),建立了結(jié)合約簡組合算法的混合因果網(wǎng),對該問題進行了知識推理分析。實驗結(jié)果表明,混合貝葉斯網(wǎng)絡(luò)雖然因模糊變量的處理增加了一些計算復(fù)雜度,但是通過結(jié)合約簡算法與IGCI—HC算法,降低了計算復(fù)雜度。另外,由于其采用了模糊概率表示變量的不確定性,解決了邊緣銳化的問題。與傳統(tǒng)的貝葉斯網(wǎng)絡(luò)相比,混合貝葉斯網(wǎng)絡(luò)在網(wǎng)絡(luò)質(zhì)量與推理精度上都要更為出色。
[Abstract]:As an effective tool to deal with causality expression and reasoning among variables, Bayesian network is widely used in artificial intelligence and data mining, but the traditional Bayesian network also has the following three problems. First of all, traditional Bayesian networks can only deal with discrete variables, and other types of variables need to be discretized first. Especially in the face of fuzzy variables, a large amount of information will be lost, which will affect the reasoning accuracy. Then the structural learning of Bayesian networks is the NP-HARD problem. In high-dimensional data, the search space of network structure will increase exponentially, and the efficiency of common structural learning algorithms will become low. Finally, the traditional structural learning algorithm can not recognize Markov equivalent class. When there are a large number of Markov equivalence classes in search space, the search efficiency will be very low, and it is difficult to converge to the optimal solution. For the first problem, this paper extends the traditional Bayesian network according to fuzzy theory. A complete scheme of hybrid Bayesian networks which can be compatible with fuzzy variables is presented. For problem two, a new reduction combination scheme is proposed by dividing the problem into multiple subnets. The influence of high-dimensional data is reduced. At the same time, in the process of clustering, the join points between subnets are determined, and the secondary search is avoided. Compared with the similar algorithm, the algorithm reduces the computational complexity. The clustering process uses the similarity based on causality to reduce the negative impact on the structure quality caused by the establishment of the subnet. Finally, the problem 3 is addressed. Then combining the causal inference based on information geometry with the mountain climbing method, an improved mountain climbing algorithm IGCI-HC is proposed. To solve the traditional structural learning algorithm can not identify the shortcomings of Markov equivalents. College asset management directly affects the construction and development of colleges and universities. There are many factors that affect the asset management of colleges and universities. It has the characteristics of high dimension and many types of factors involved. How to mine the causal relationship between factors and asset management efficiency and give knowledge reasoning decision assistance. According to the related data of asset management in 72 colleges and universities, a hybrid causality net combining reduction combination algorithm is established, and the knowledge reasoning analysis of this problem is carried out. The experimental results show that. Although the hybrid Bayesian network increases some computational complexity because of the fuzzy variable processing, it reduces the computational complexity by combining the reduction algorithm with the IGCI-HC algorithm. Because of the uncertainty of fuzzy probability representation variables, the problem of edge sharpening is solved. Compared with traditional Bayesian networks, hybrid Bayesian networks are better in network quality and reasoning accuracy.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP18;G647.5

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