【摘要】：粗糙集理論的出發(fā)點(diǎn)是根據(jù)現(xiàn)有的知識(shí)對(duì)未知信息進(jìn)行劃分,然后確定每一個(gè)劃分類對(duì)某一概念的支持程度,并用正域、負(fù)域和邊界域這三個(gè)近似集合來(lái)表示,之后,通過(guò)屬性約簡(jiǎn)和屬性值約簡(jiǎn)算法獲取決策規(guī)則。本文首先提出一種基于條件先驗(yàn)概率優(yōu)勢(shì)關(guān)系的粗糙集模型,此模型是建立在對(duì)不完備偏序關(guān)系決策系統(tǒng)屬性值數(shù)據(jù)統(tǒng)計(jì)基礎(chǔ)上的,既考慮到同一屬性取值的不同情況又考慮到不同屬性之間的關(guān)聯(lián)性,使得各種先驗(yàn)信息能夠充分利用,因此有效地提高了分類精度和分類質(zhì)量。其次,由于基于知識(shí)粒度的不確定性度量方法不能精確、系統(tǒng)地反映系統(tǒng)的不確定性,為此本文提出一種新的基于邊界域和知識(shí)粒度的改進(jìn)粗糙熵。改進(jìn)粗糙熵不僅考慮到因劃分不精確所產(chǎn)生的不確定性,而且顧及到了由邊界域的變化所帶來(lái)的不確定性,從而使不確定性度量值的計(jì)算更加精確,為條件先驗(yàn)概率優(yōu)勢(shì)關(guān)系模型中不確定性度量問(wèn)題的研究開(kāi)拓了新的思路。最后,本文介紹了約簡(jiǎn)、分布約簡(jiǎn)和分配約簡(jiǎn),并詳細(xì)分析了三者之間的關(guān)系和它們的性質(zhì)。同時(shí),提出了基于改進(jìn)粗糙熵的啟發(fā)式約簡(jiǎn)算法和基于目標(biāo)分配矩陣的分配約簡(jiǎn)算法。理論分析表明,后者因在求取約簡(jiǎn)過(guò)程過(guò)于繁瑣而降低了搜索效率,而前者在約簡(jiǎn)過(guò)程中,直接刪除系統(tǒng)中不必要的屬性,因此節(jié)省了搜索時(shí)間,提高了搜索效率。
[Abstract]:The starting point of rough set theory is to divide the unknown information according to the existing knowledge, then determine the degree of support of each partition class to a certain concept, and express it with three approximate sets: positive domain, negative domain and boundary domain. Decision rules are obtained by attribute reduction and attribute value reduction. In this paper, a rough set model based on conditional priori probability dominance relation is proposed, which is based on the statistics of attribute values of incomplete partial order decision system. Considering not only the different values of the same attribute but also the correlation between different attributes, all kinds of prior information can be fully utilized, so that the classification accuracy and classification quality are improved effectively. Secondly, because the uncertainty measurement method based on knowledge granularity is not accurate, it systematically reflects the uncertainty of the system. Therefore, a new improved rough entropy based on boundary domain and knowledge granularity is proposed in this paper. The improved rough entropy takes into account not only the uncertainty caused by the inaccuracy of partition, but also the uncertainty caused by the change of boundary domain, which makes the calculation of uncertainty measure more accurate. It opens up a new idea for the study of uncertainty measurement in conditional priori probabilistic advantage relation model. Finally, this paper introduces the reduction, distribution reduction and distribution reduction, and analyzes the relations among them and their properties in detail. At the same time, a heuristic reduction algorithm based on improved rough entropy and an assignment reduction algorithm based on objective assignment matrix are proposed. The theoretical analysis shows that the latter reduces the search efficiency because the process of obtaining reduction is too cumbersome, while the former directly removes unnecessary attributes from the system in the process of reduction, so the search time is saved and the search efficiency is improved.
【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TP311.13

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本文編號(hào)：2168545