本文選題：擴(kuò)展粗糙集 + 優(yōu)勢(shì)關(guān)系��； 參考：《安徽工業(yè)大學(xué)》2017年碩士論文

【摘要】：粗糙集理論是波蘭數(shù)學(xué)家Pawlak提出的能有效處理不確定性數(shù)據(jù)的數(shù)學(xué)工具,相比較于模糊集的隸屬度函數(shù)的確定及證據(jù)理論的基本概率賦值(BPA)的確定,粗集模型無(wú)需任何先驗(yàn)知識(shí)及假設(shè),而僅僅需要基礎(chǔ)數(shù)據(jù)即可,因而在多屬性決策問(wèn)題中的指標(biāo)選擇和方案排序選優(yōu)等方面有很好的應(yīng)用潛力。經(jīng)典粗糙集理論是基于等價(jià)關(guān)系的,該關(guān)系對(duì)數(shù)據(jù)的要求較為嚴(yán)格,導(dǎo)致經(jīng)典粗糙集在實(shí)際應(yīng)用中存在諸多缺陷,本文以經(jīng)典粗糙集為基礎(chǔ)延伸出了兩類(lèi)擴(kuò)展粗糙集,分別是基于優(yōu)勢(shì)關(guān)系的粗糙集以及與支持向量機(jī)相結(jié)合的雜合粗糙集,并根據(jù)這兩類(lèi)擴(kuò)展粗糙集的優(yōu)勢(shì)去解決特定的不確定性多屬性決策問(wèn)題,并取得了不錯(cuò)的效果。在處理實(shí)際問(wèn)題時(shí),有許多決策問(wèn)題是基于優(yōu)勢(shì)關(guān)系的。例如,對(duì)于兩家上市公司而言,其大部分的財(cái)務(wù)指標(biāo)都是帶有偏好的,投資者更傾向于關(guān)注資產(chǎn)負(fù)債率低而投資回報(bào)率高的企業(yè)。對(duì)于這種情況,屬性值的偏好也是一種重要的決策信息,而經(jīng)典的粗糙集理論不能有效處理該類(lèi)問(wèn)題。本文先給出優(yōu)勢(shì)關(guān)系粗糙集的基本概念以及性質(zhì),利用信息熵以及互信息的知識(shí)給出了其約簡(jiǎn)方法,并在此基礎(chǔ)上結(jié)合證據(jù)理論給出了對(duì)象的不確定性推理。同時(shí)考慮到實(shí)際問(wèn)題中,有些決策系統(tǒng)的數(shù)據(jù)需要用區(qū)間值表示,本文利用區(qū)間數(shù)與可能度的關(guān)系,提出了基于可能度優(yōu)勢(shì)關(guān)系的區(qū)間序粗糙集模型,再結(jié)合優(yōu)勢(shì)度的知識(shí),能夠很好地處理備選方案的排序問(wèn)題。另外,粗糙集在實(shí)際應(yīng)用中對(duì)于數(shù)據(jù)的敏感度較高,而在現(xiàn)實(shí)的情況中,由于數(shù)據(jù)的收集以及數(shù)據(jù)各種處理比較難以精確控制,導(dǎo)致粗糙集在作為信息識(shí)別系統(tǒng)時(shí)的預(yù)測(cè)精度不是很讓人滿意。同時(shí),考慮到支持向量機(jī)以結(jié)構(gòu)化風(fēng)險(xiǎn)最小化為原則而使其有很強(qiáng)的泛化能力。此外,SVM算法能夠較好地解決小樣本學(xué)習(xí)問(wèn)題以及能夠有效處理“維數(shù)災(zāi)難”問(wèn)題。因此,本文將考慮將兩者有機(jī)結(jié)合,一方面利用粗糙集有效的屬性約簡(jiǎn)能力,一方面利用支持向量機(jī)的高精度預(yù)測(cè)能力,并用來(lái)處理個(gè)人信用評(píng)估這一實(shí)際問(wèn)題。
[Abstract]:Rough set theory is a mathematical tool put forward by Pawlak, a Polish mathematician, which can deal with uncertain data effectively. Compared with the determination of membership function of fuzzy sets and the determination of basic probability assignment (BPA) of evidence theory, rough set theory can deal with uncertain data effectively. Rough set model does not need any prior knowledge and hypothesis, but only needs basic data, so it has a good application potential in multi-attribute decision making problems such as index selection and scheme ranking selection. The classical rough set theory is based on the equivalence relation, which requires strict data, which leads to many defects in the practical application of classical rough set. In this paper, two kinds of extended rough sets are extended based on classical rough set. Rough sets based on dominance relationship and hybrid rough sets combined with support vector machine are used to solve the uncertain multi-attribute decision making problem according to the advantages of these two kinds of extended rough sets and good results are obtained. When dealing with practical problems, there are many decision-making problems based on advantage relationship. For example, for two listed companies, most of their financial indicators are biased, with investors more likely to focus on companies with low asset-liability ratios and high returns on investment. In this case, the preference of attribute value is also an important decision information, but the classical rough set theory can not deal with this kind of problem effectively. In this paper, the basic concepts and properties of the rough set of dominance relations are given, and the reduction method is given by using the knowledge of information entropy and mutual information, and the uncertainty reasoning of the object is given based on the evidence theory. At the same time, considering the practical problems, some data of decision system need to be represented by interval value. In this paper, an interval order rough set model based on the dominance relation of possibility degree is put forward by using the relation between interval number and possibility degree, and then the knowledge of dominance degree is combined. It can deal with the scheduling problem of alternatives well. In addition, rough set is sensitive to data in practical application, but in reality, it is difficult to accurately control data collection and data processing. The prediction accuracy of rough set as an information recognition system is not satisfactory. At the same time, the support vector machine (SVM) has a strong generalization ability based on the principle of structural risk minimization. In addition, SVM algorithm can solve the problem of small sample learning and effectively deal with the problem of "dimension disaster". Therefore, this paper will consider the combination of the two methods. On the one hand, we will make use of the effective attribute reduction ability of rough set; on the other hand, we will use the high precision prediction ability of support vector machine, and use it to deal with the practical problem of personal credit evaluation.
【學(xué)位授予單位】：安徽工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F406.7;F425;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 樊自甫;王麗花;孫紅;;基于優(yōu)勢(shì)粗糙集構(gòu)建突發(fā)事件應(yīng)急通信分級(jí)模型[J];統(tǒng)計(jì)與決策;2015年18期

2 廖勇;陳曄;徐海燕;;基于優(yōu)勢(shì)粗糙集的中小企業(yè)二維績(jī)效評(píng)價(jià)模型[J];工業(yè)工程;2015年01期

3 鄧維斌;王國(guó)胤;;基于優(yōu)勢(shì)關(guān)系粗糙集的電信客戶價(jià)值評(píng)價(jià)方法[J];計(jì)算機(jī)應(yīng)用研究;2015年06期

4 王虎;王銳;曾珠;;基于粗糙集與D_S證據(jù)理論的汽車(chē)售后服務(wù)知識(shí)推理[J];工業(yè)工程;2014年05期

5 羅凱;吳超;陽(yáng)富強(qiáng);;基于優(yōu)勢(shì)關(guān)系粗糙集與灰靶決策的礦石自燃管理應(yīng)對(duì)體系[J];中南大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年01期

6 張濤;路靜;任宏雷;;一種基于樹(shù)圖的屬性約簡(jiǎn)算法[J];小型微型計(jì)算機(jī)系統(tǒng);2014年01期

7 劉健;陳劍;劉思峰;周獻(xiàn)中;;風(fēng)險(xiǎn)偏好與屬性約簡(jiǎn)在決策問(wèn)題中的應(yīng)用研究[J];管理科學(xué)學(xué)報(bào);2013年08期

8 鮑新中;楊宜;;基于聚類(lèi)-粗糙集-神經(jīng)網(wǎng)絡(luò)的企業(yè)財(cái)務(wù)危機(jī)預(yù)警[J];系統(tǒng)管理學(xué)報(bào);2013年03期

9 劉健;劉思峰;吳順祥;;基于優(yōu)勢(shì)關(guān)系的多屬性決策對(duì)象排序研究[J];控制與決策;2012年04期

10 丁衛(wèi)平;王建東;管致錦;;基于量子蛙跳協(xié)同進(jìn)化的粗糙屬性快速約簡(jiǎn)[J];電子學(xué)報(bào);2011年11期

相關(guān)博士學(xué)位論文 前2條

1 閻瑞霞;粗糙集的論域擴(kuò)展理論及在專(zhuān)家系統(tǒng)中的應(yīng)用[D];東華大學(xué);2012年

2 滕書(shū)華;基于粗糙集理論的不確定性度量和屬性約簡(jiǎn)方法研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2010年

，

本文編號(hào)：2067394