本文選題：大數(shù)據(jù)　切入點(diǎn)：粗糙集　出處：《太原理工大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：大數(shù)據(jù)處理技術(shù)的迅速發(fā)展,極大的改變了人們的生活習(xí)慣、工作方式和思維模式。專(zhuān)家和學(xué)者們也認(rèn)識(shí)到海量數(shù)據(jù)分析和處理的廣闊前景,并希望能夠從中得到有用的信息。大數(shù)據(jù)往往具有不確定、維數(shù)大和不完備等特點(diǎn),而粗糙集作為處理不確定、不一致問(wèn)題的有效工具,已經(jīng)廣泛的應(yīng)用在數(shù)據(jù)挖掘中。針對(duì)粗糙集常用于處理完備信息系統(tǒng)的問(wèn)題,人們致力于尋找更好的擴(kuò)展方法并將其應(yīng)用于不完備信息系統(tǒng)。本文使用“鄰域”和“容差完備度”的概念對(duì)經(jīng)典粗糙集進(jìn)行了擴(kuò)展,得到了新的鄰域模型,并將該模型應(yīng)用于目標(biāo)遮擋的圖像分類(lèi)當(dāng)中。通過(guò)在Pawlak經(jīng)典粗糙集的基礎(chǔ)上引入了粗糙集的鄰域模型,用于解決離散型屬性和連續(xù)型屬性的混合數(shù)據(jù)類(lèi)型不能同時(shí)處理的問(wèn)題,并介紹了鄰域粗糙集(Neighborhood Rough Set,NR)的基本概念。而不具有容差能力的方法在處理不完備的信息時(shí)難以達(dá)到理想的效果,因此,本文給出了擴(kuò)展容差關(guān)系(Extended Tolerance Relation,ETR)模型。該模型將限制條件設(shè)為鄰域和容差完備度,并以擴(kuò)展容差鄰域?yàn)榛A(chǔ)選擇決策正域,經(jīng)計(jì)算得到系統(tǒng)的屬性重要度,最后給出基于擴(kuò)展容差關(guān)系的屬性約簡(jiǎn)算法,并通過(guò)刪除冗余來(lái)降低噪聲數(shù)據(jù)對(duì)分類(lèi)結(jié)果產(chǎn)生的影響。變換不同的鄰域閾值,將單個(gè)樣本應(yīng)用于不同的分類(lèi)器,并分析實(shí)驗(yàn)結(jié)果。使用UCI庫(kù)上的幾組混合類(lèi)型數(shù)據(jù)集進(jìn)行實(shí)驗(yàn),并與其他幾種擴(kuò)展粗糙集算法進(jìn)行對(duì)比。通過(guò)分析各算法在不同分類(lèi)器下的精度變化趨勢(shì)可知,ETR算法能在不降低分類(lèi)精度的同時(shí)保持較少的約簡(jiǎn)。因此驗(yàn)證了擴(kuò)展容差鄰域模型的有效性及算法的可行性。本文采用顏色和紋理相融合的方法將擴(kuò)展容差關(guān)系應(yīng)用在目標(biāo)遮擋的場(chǎng)景圖像分類(lèi)中。首先,構(gòu)建遮擋圖像對(duì)象集知識(shí)表示系統(tǒng),面向?qū)ο蠹到y(tǒng),使用擴(kuò)展容差鄰域模型建立圖像邊緣及遮擋邊界的相容粒度空間;其次,計(jì)算相容粒度空間下的顏色特征直方圖,得到相容粒的直方圖統(tǒng)計(jì)特征;最后,在多種對(duì)比算法下,使用不同分類(lèi)器對(duì)本文算法進(jìn)行驗(yàn)證。實(shí)驗(yàn)結(jié)果表明該方法在解決復(fù)雜場(chǎng)景圖像中遮擋問(wèn)題的有效性,實(shí)現(xiàn)了場(chǎng)景圖像的分類(lèi)與檢索。
[Abstract]:The rapid development of big data's processing technology has greatly changed people's living habits, working methods and thinking patterns. Experts and scholars have also recognized the broad prospect of massive data analysis and processing. Big data often has the characteristics of uncertainty, dimension and incompleteness, and rough set is an effective tool to deal with the problem of uncertainty and inconsistency. Has been widely used in data mining. Rough sets are often used to deal with the problem of complete information systems, In this paper, the concepts of "neighborhood" and "tolerance completeness" are used to extend the classical rough set, and a new neighborhood model is obtained. The model is applied to the image classification of target occlusion. Based on the classical Pawlak rough set, the neighborhood model of rough set is introduced to solve the problem that the mixed data types of discrete and continuous attributes can not be processed at the same time. The basic concept of neighborhood Rough set (NRs) is introduced. The method without tolerance ability is difficult to achieve ideal results when dealing with incomplete information. In this paper, the extended tolerance relation extended Tolerance relation model is presented. The model sets the constraint conditions as neighborhood and tolerance completeness, and selects the decision positive domain based on extended tolerance neighborhood. The attribute importance of the system is calculated. Finally, an attribute reduction algorithm based on extended tolerance relationship is presented, and the influence of noise data on classification results is reduced by deleting redundancy. Different neighborhood thresholds are transformed, and a single sample is applied to different classifiers. The experimental results are analyzed. Several sets of mixed data sets on UCI library are used to carry out experiments. And compared with other extended rough set algorithms, by analyzing the trend of the accuracy of each algorithm under different classifiers, we can see that the ETR algorithm can keep less reduction without reducing the classification accuracy. Therefore, it is verified that the extension algorithm can reduce the classification accuracy at the same time. The validity of the neighborhood model and the feasibility of the algorithm. In this paper, the extended tolerance relationship is applied to the target occlusion scene image classification using the method of color and texture fusion. The knowledge representation system of occlusion image object set and object oriented set system are constructed, and the compatible granularity space of image edge and occlusion boundary is established by using extended tolerance neighborhood model. Secondly, the color feature histogram under consistent granularity space is calculated. The histogram statistical features of compatible particles are obtained. Finally, different classifiers are used to verify the algorithm under various contrast algorithms. The experimental results show that the proposed method is effective in solving the occlusion problem in complex scene images. The classification and retrieval of scene images are realized.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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