本文選題：支持向量機(jī)　切入點(diǎn)：人口失衡　出處：《廣西民族大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：2011年,我國“十二五”規(guī)劃綱要提出“促進(jìn)人口長期均衡發(fā)展”的戰(zhàn)略目標(biāo),使得人口失衡風(fēng)險(xiǎn)預(yù)警成為當(dāng)今人口領(lǐng)域研究的新課題。 人口失衡風(fēng)險(xiǎn)的表現(xiàn)形式復(fù)雜,理論上可分為240種不同類型,具有內(nèi)生性、擴(kuò)散性等特征。人口失衡風(fēng)險(xiǎn)問題的復(fù)雜性,增大了人口失衡風(fēng)險(xiǎn)預(yù)警的難度,用傳統(tǒng)的統(tǒng)計(jì)評估方法或系統(tǒng)評估方法均難以取得滿意效果。 本文研究基于支持向量機(jī)的人口失衡風(fēng)險(xiǎn)預(yù)警方法。支持向量機(jī)技術(shù)適于處理小樣本、非線性和高維數(shù)據(jù),并且具有優(yōu)良的泛化性能,切合人口失衡風(fēng)險(xiǎn)預(yù)警的技術(shù)需求。首先,在剖析人口失衡風(fēng)險(xiǎn)點(diǎn)的基礎(chǔ)上,抽取出能表征人口失衡風(fēng)險(xiǎn)的指標(biāo),構(gòu)建人口失衡風(fēng)險(xiǎn)預(yù)警指標(biāo)體系并制定指標(biāo)預(yù)警閾值。其次,將支持向量機(jī)、粗糙集、決策樹等智能計(jì)算方法引入人口風(fēng)險(xiǎn)預(yù)警領(lǐng)域,構(gòu)造模塊化的基于支持向量機(jī)的人口失衡風(fēng)險(xiǎn)預(yù)警模型。該模型的一個(gè)模塊是一組標(biāo)準(zhǔn)支持向量機(jī)和粗糙集支持向量機(jī),用于實(shí)現(xiàn)總體風(fēng)險(xiǎn)警度預(yù)報(bào)；另一個(gè)模塊是一組決策樹支持向量機(jī),用于實(shí)現(xiàn)風(fēng)險(xiǎn)的分項(xiàng)評估,根據(jù)分項(xiàng)評估結(jié)果可準(zhǔn)確定位樣本風(fēng)險(xiǎn)點(diǎn)。第三,針對難分類樣本,構(gòu)造由分項(xiàng)風(fēng)險(xiǎn)評估結(jié)果預(yù)測總體風(fēng)險(xiǎn)警度的粗糙集核驗(yàn)?zāi)Ｐ�。以分�?xiàng)風(fēng)險(xiǎn)評估結(jié)果作為條件屬性,以總體風(fēng)險(xiǎn)警度作為決策屬性,構(gòu)建決策表,利用粗糙集算法生成決策規(guī)則,對難分類數(shù)據(jù)進(jìn)行分類,以此結(jié)果核驗(yàn)原粗糙集支持向量機(jī)分類結(jié)果的準(zhǔn)確性。這一算法也可以在其他涉及分項(xiàng)知識的專業(yè)領(lǐng)域當(dāng)中應(yīng)用。
[Abstract]:In 2011, the outline of the 12th Five-Year Plan put forward the strategic goal of "promoting the long-term balanced development of population", which made the risk early warning of population imbalance become a new topic in the field of population. The manifestation of population imbalance risk is complex, which can be divided into 240 different types in theory, which has the characteristics of endogenicity and diffusivity. The complexity of population imbalance risk makes it more difficult to predict population imbalance risk. It is difficult to obtain satisfactory results by using traditional statistical evaluation methods or systematic evaluation methods. In this paper, the population imbalance risk early warning method based on support vector machine (SVM) is studied. Support vector machine (SVM) is suitable for processing small sample, nonlinear and high dimensional data, and has excellent generalization performance. First of all, on the basis of analyzing the risk point of population imbalance, we extract the index which can represent the risk of population imbalance, construct the early warning index system of population imbalance risk and establish the warning threshold. The intelligent computing methods such as support vector machine, rough set and decision tree are introduced into the field of population risk early warning. A modular population imbalance risk early warning model based on support vector machine (SVM) is constructed, one of the modules of the model is a set of standard support vector machines and rough set support vector machines, which can be used to predict the overall risk alarm. Another module is a set of decision tree support vector machines, which can be used to realize the sub-assessment of risk. According to the results of sub-evaluation, the risk points of samples can be accurately located. A rough set verification model is constructed to predict the overall risk alarm from the results of the itemized risk assessment. The decision table is constructed with the result of the itemized risk assessment as the conditional attribute and the total risk alarm as the decision attribute. Rough set algorithm is used to generate decision rules to classify difficult classification data, which verifies the accuracy of classification results based on rough set support vector machine. This algorithm can also be used in other specialized fields involving sub-knowledge.
【學(xué)位授予單位】：廣西民族大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：C924.2;TP18

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本文編號：1622322