本文關(guān)鍵詞： 粗糙集 優(yōu)勢類 正域 辨識矩陣 快速約簡　出處：《河北大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科學(xué)技術(shù)的進(jìn)步與發(fā)展,每天產(chǎn)生數(shù)以萬計(jì)的數(shù)據(jù),如何從這些數(shù)據(jù)中挖掘出重要的、有用的信息顯得尤為重要。在實(shí)際問題中,描述對象的屬性值大多是具有偏好順序的,而粗糙集理論的核心問題一直是屬性約簡及其效率的提高,因此優(yōu)勢關(guān)系下的快速屬性約簡方法的研究是非常有意義的。在優(yōu)勢關(guān)系下粗糙集模型的屬性約簡方法中,計(jì)算優(yōu)勢類作為基本粒進(jìn)而計(jì)算上下近似集是必不可少的步驟,而傳統(tǒng)算法需要比較所有樣例的每個(gè)屬性值,耗時(shí)較大,近似集、屬性約簡的計(jì)算效率受到了影響。大部分已有的加速算法都是針對等價(jià)類的計(jì)算,只能用于處理符號值屬性�；诖�,本文根據(jù)優(yōu)勢類的性質(zhì)提出了一種快速縮減搜索空間的思想,并在此基礎(chǔ)上設(shè)計(jì)相應(yīng)的算法來提高優(yōu)勢類的計(jì)算效率,從而提高屬性約簡的性能,尤其適用于屬性數(shù)量較多的情況。進(jìn)一步,在快速求得優(yōu)勢類的基礎(chǔ)上將優(yōu)勢類和決策類表示為01矩陣,采用或運(yùn)算計(jì)算正域,利用辨識矩陣求得屬性約簡。最后通過UCI數(shù)據(jù)集的實(shí)驗(yàn)表明,該方法在與傳統(tǒng)方法保持相同約簡集的情況下,減少了計(jì)算時(shí)間,提高了屬性約簡的效率。
[Abstract]:With the progress and development of science and technology, tens of thousands of data are produced every day. It is very important to extract important and useful information from these data. Most of the attribute values of describing objects have preference order, but the core problem of rough set theory is attribute reduction and the improvement of its efficiency. Therefore, it is very meaningful to study the fast attribute reduction method in the dominant relation. In the attribute reduction method of rough set model under the advantage relation, it is necessary to calculate the superior class as the basic particle and then calculate the upper and lower approximate set. However, the traditional algorithm needs to compare each attribute value of all the samples, which is time-consuming, approximate set, and the efficiency of attribute reduction is affected. It can only be used to deal with symbolic value attributes. Based on this, this paper puts forward an idea of reducing search space quickly according to the properties of superior classes, and then designs corresponding algorithms to improve the computational efficiency of superior classes. In order to improve the performance of attribute reduction, especially in the case of a large number of attributes. Further, the dominant class and decision class are expressed as 01 matrix on the basis of fast finding the superior class, and the positive domain is calculated by or by calculation. Finally, the experiment of UCI data set shows that the method can reduce the computation time and improve the efficiency of attribute reduction by keeping the same reduction set as the traditional method.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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