本文選題：粗糙集 + 屬性約簡�。� 參考：《江蘇科技大學》2017年碩士論文

【摘要】：粗糙集理論作為智能信息處理的一種重要方法,由波蘭科學家Pawlak首先提出,引起大量學者關注并對其開展研究。在研究過程中,學者們?yōu)榱舜蚱拼植诩瘧脠鼍暗木窒扌?提出了一系列的粗糙集擴展模型。值得一提的是,其中有兩個粗糙集模型應用十分廣泛,即:模糊粗糙集模型、決策粗糙集模型。前者用于改善粗糙集理論在處理模糊問題的乏力狀況,使得粗糙集理論在模糊性問題的處理方面也有了較好的能力;后者是一個借助貝葉斯風險決策理論進行改進的粗糙集模型,它給粗糙集引入語義解釋,極大地緩解了粗糙集解決問題缺乏科學語義支撐的尷尬局面。同時,它也消除了經典粗糙集零錯誤容忍率和現實應用中存在錯誤率的矛盾。隨著這兩個改進粗糙集模型的廣受歡迎,作為粗糙集理論的核心內容之一的屬性約簡,其研究的價值也變得越來越大。針對以上研究問題,本文擬從模型的理論和應用兩個方面開展該研究工作。主要研究內容如下:(1)針對模糊粗糙集模型,我們從測試代價出發(fā),根據模糊粗糙集模型的特性,分析測試代價敏感的屬性約簡方法的實現方法,并給出兩種不同的算法思想,定義出其相對應的基于降低測試代價原則的算法,并通過實驗對理論進行驗證這兩種算法在處理該問題的效率。從實驗結果我們可以發(fā)現,遺傳算法在處理該問題的時候在不考慮時間的情況下能得到更好的結果。(2)針對決策粗糙集模型,我們從決策規(guī)則的角度出發(fā),對決策保持屬性約簡和決策單調屬性約簡方法進行分析。將局部約簡的方法引入決策粗糙集模型下,定義出局部決策保持和決策單調屬性約簡的方法,分析方法的可行性。然后再給出算法思想,并依據算法思想,進行實驗,進一步對理論的實際可行性進行驗證。從實驗結果我們可以發(fā)現,局部的決策保持以及決策單調約簡算法分別在降低冗余屬性和獲取決策規(guī)則兩個方面相較于全局屬性約簡具有更好的處理能力。
[Abstract]:As an important method of intelligent information processing, rough set theory was first proposed by Polish scientist Pawlak. In order to break the limitation of rough set application scenario, scholars put forward a series of rough set extension models. It is worth mentioning that two rough set models are widely used, namely, fuzzy rough set model and decision rough set model. The former is used to improve the weakness of rough set theory in dealing with fuzzy problems, which makes rough set theory have better ability in dealing with fuzzy problems. The latter is an improved rough set model based on Bayesian risk decision theory, which introduces semantic interpretation to rough set, which greatly alleviates the awkward situation of rough set problem solving lacking scientific semantic support. At the same time, it eliminates the contradiction between the zero error tolerance rate of classical rough set and the error rate in practical application. With the popularity of these two improved rough set models, attribute reduction, which is one of the core contents of rough set theory, has become more and more valuable. In view of the above problems, this paper intends to carry out the research from the theory and application of the model. The main research contents are as follows: (1) in view of fuzzy rough set model, we analyze the implementation method of attribute reduction method which is sensitive to test cost according to the characteristics of fuzzy rough set model, and give two different algorithms. The corresponding algorithms based on the principle of reducing test cost are defined, and the efficiency of the two algorithms in dealing with this problem is verified by experiments. From the experimental results, we can find that the genetic algorithm can get better results when dealing with this problem without considering the time. (2) for the decision rough set model, we start from the angle of decision rules. The methods of decision preserving attribute reduction and decision monotone attribute reduction are analyzed. The method of local reduction is introduced into the decision rough set model, and the methods of local decision retention and monotone attribute reduction are defined, and the feasibility of the method is analyzed. Then the idea of the algorithm is given and the experiment is carried out according to the idea of the algorithm to verify the practical feasibility of the theory. From the experimental results we can find that local decision retention and decision monotone reduction algorithms have better processing ability than global attribute reduction algorithms in reducing redundant attributes and obtaining decision rules respectively.
【學位授予單位】：江蘇科技大學
【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：1947210