【摘要】：實(shí)際應(yīng)用中,隨著各種數(shù)據(jù)觀測工具、實(shí)驗(yàn)設(shè)備效率與性能的提高,以及對于數(shù)據(jù)的搜集方式與處理方法的多樣性增長,我們獲取到的數(shù)據(jù)普遍呈現(xiàn)持續(xù)增長、不斷更新、不斷細(xì)致化的動態(tài)現(xiàn)象。數(shù)據(jù)的不斷更新與細(xì)致化,將會導(dǎo)致原有信息粒、知識結(jié)構(gòu)的動態(tài)變化,時效性成為動態(tài)數(shù)據(jù)環(huán)境中知識獲取的關(guān)鍵問題。Wu對于對象在不同尺度下?lián)碛胁煌闹R粒度,提出了多標(biāo)記信息系統(tǒng)。仔細(xì)觀察不難發(fā)現(xiàn),這樣的多標(biāo)記信息系統(tǒng)其實(shí)就是一個信息不斷更新變化的過程。然而在現(xiàn)實(shí)生活中,雖然足夠詳細(xì)的數(shù)據(jù)信息能夠使我們更加清晰地認(rèn)識事物,但在某種程度下,搜集處理這些信息的代價也是很大的,甚至龐大的信息中存在的無用價值信息也會過多地耗費(fèi)成本,這種情況下再去不必要地細(xì)致化信息是得不償失的,因而我們把序貫三支決策引入到多標(biāo)記信息系統(tǒng),在多標(biāo)記決策信息系統(tǒng)下研究了通過動態(tài)決策來選取最優(yōu)標(biāo)記的相關(guān)問題。最優(yōu)標(biāo)記選擇是多標(biāo)記決策信息系統(tǒng)中的一個重要問題,然而由于現(xiàn)實(shí)生活中我們所研究的多標(biāo)記決策信息系統(tǒng)往往會出現(xiàn)對象更新和屬性更新的情形,因而現(xiàn)有的最優(yōu)標(biāo)記選擇方法并不總是適用的。同時我們也發(fā)現(xiàn),序貫三支決策是研究信息更新的一個重要手段,因此在本文中,我們用序貫三支決策方法研究了動態(tài)多標(biāo)記決策信息系統(tǒng)下的最優(yōu)標(biāo)記選擇問題。詳細(xì)來說,首先我們將序貫三支決策引入多標(biāo)記信息系統(tǒng),這樣的多標(biāo)記信息系統(tǒng)可以看作是論域的多粒度表示。然后,加入決策屬性就有了多標(biāo)記決策信息系統(tǒng),類似的引入序貫三支決策。最后,考慮到多標(biāo)記決策信息系統(tǒng)下的數(shù)據(jù)更新,我們研究了數(shù)據(jù)更新下不確定域的變化情形,進(jìn)而分析了最優(yōu)尺度的變化問題。同時,我們也在最后給出了一些數(shù)據(jù)實(shí)驗(yàn)來驗(yàn)證本文最優(yōu)尺度選擇的有效性。
[Abstract]:In practical applications, with the improvement of the efficiency and performance of various data observation tools, experimental equipment, and the diversity of data collection and processing methods, the data we have obtained have generally been continuously growing and updating. The dynamic phenomenon of constant refinement. The continuous updating and meticulous of data will lead to the dynamic change of original information grain and knowledge structure, and timeliness becomes the key problem of knowledge acquisition in dynamic data environment. Wu has different granularity of knowledge for objects at different scales. A multi-label information system is proposed. It is not difficult to observe that such a multi-label information system is a process of information updating and changing. However, in real life, while sufficiently detailed data can enable us to understand things more clearly, to some extent, the cost of collecting and processing this information is also enormous. Even the useless value information that exists in the huge information will consume too much cost. In this case, it is not worth the gain to turn the information into detail unnecessarily, so we introduce three sequential decisions into the multi-label information system. In this paper, the problem of selecting the optimal label by dynamic decision is studied in the multi-label decision information system. Optimal label selection is an important problem in multi-label decision information systems. However, the multi-label decision information systems we study in real life often have object update and attribute update. Therefore, the existing optimal tag selection methods are not always applicable. At the same time, we also find that sequential three-branch decision making is an important means to study information updating. In this paper, we use sequential three-branch decision method to study the optimal label selection problem in dynamic multi-label decision information system. In detail, first of all, we introduce the sequential three-branch decision into the multi-label information system, which can be regarded as the multi-granularity representation of the domain. Then, there is a multi-label decision information system by adding decision attributes, which is similar to the sequential three-branch decision-making. Finally, considering the data update in multi-label decision information system, we study the change of uncertain domain under data update, and then analyze the change of optimal scale. At the same time, we also give some data experiments to verify the effectiveness of the optimal scale selection.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：N945.25

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 劉盾;姚一豫;李天瑞;;三枝決策粗糙集[J];計(jì)算機(jī)科學(xué);2011年01期

2 李華雄;周獻(xiàn)中;黃兵;趙佳寶;;決策粗糙集與代價敏感分類[J];計(jì)算機(jī)科學(xué)與探索;2013年02期

3 田海龍;朱艷輝;梁韜;馬進(jìn);劉t，

本文編號：2318552