本文選題：支持向量機(jī) + bagging��； 參考：《浙江財(cái)經(jīng)大學(xué)》2017年碩士論文

【摘要】：中國(guó)經(jīng)濟(jì)的快速發(fā)展,提高了居民信用消費(fèi)能力�；ヂ�(lián)網(wǎng)金融的快速發(fā)展,為居民信用消費(fèi)提供了便利,個(gè)人住房按揭貸款、個(gè)人小額貸款、信用卡消費(fèi)貸款等信貸產(chǎn)品如雨后春筍般涌現(xiàn)。隨著中國(guó)經(jīng)濟(jì)進(jìn)一步信用化,信用消費(fèi)拉動(dòng)經(jīng)濟(jì)增長(zhǎng)的作用進(jìn)一步凸顯,居民信用消費(fèi)意愿和能力正穩(wěn)步上升。國(guó)內(nèi)各互聯(lián)網(wǎng)金融機(jī)構(gòu)紛紛把個(gè)人消費(fèi)貸款業(yè)務(wù)作為未來(lái)的發(fā)展戰(zhàn)略之一。但是,國(guó)內(nèi)的互聯(lián)網(wǎng)金融機(jī)構(gòu)對(duì)個(gè)人消費(fèi)貸款的風(fēng)險(xiǎn)管理水平相對(duì)較低,管理手段和方法還比較落后。此外,互聯(lián)網(wǎng)金融機(jī)構(gòu)不存在有效的個(gè)人信用評(píng)估方法,這嚴(yán)重阻礙了個(gè)人信貸業(yè)務(wù)的發(fā)展。有效的信用評(píng)估模型不僅能增加互聯(lián)網(wǎng)金融機(jī)構(gòu)的利潤(rùn),而且還能擴(kuò)大互聯(lián)網(wǎng)金融機(jī)構(gòu)的信貸規(guī)模。因此,個(gè)人信用評(píng)估方法的研究意義重大。在互聯(lián)網(wǎng)金融時(shí)代,信用數(shù)據(jù)獲取的方式發(fā)生了改變,不僅可以從傳統(tǒng)的金融機(jī)構(gòu)獲取信貸數(shù)據(jù),還可以從電商平臺(tái)獲取電商數(shù)據(jù)以及從社交平臺(tái)獲取社交數(shù)據(jù)。伴隨而來(lái)的是信用數(shù)據(jù)規(guī)模的大幅度增長(zhǎng),信用評(píng)級(jí)業(yè)務(wù)面臨著巨大的機(jī)遇和挑戰(zhàn),如果缺乏大數(shù)據(jù)的處理能力,就無(wú)法充分挖掘潛藏在海量信用數(shù)據(jù)背后的價(jià)值�；ヂ�(lián)網(wǎng)金融機(jī)構(gòu)已經(jīng)使用定量模型來(lái)評(píng)價(jià)消費(fèi)者個(gè)人的信用風(fēng)險(xiǎn),它的研究重點(diǎn)之一是信用評(píng)價(jià)模型。支持向量機(jī)是數(shù)據(jù)驅(qū)動(dòng)型模型,它在監(jiān)督式學(xué)習(xí)過(guò)程中對(duì)數(shù)據(jù)處理,不需要對(duì)數(shù)據(jù)做特別的假設(shè)。當(dāng)數(shù)據(jù)量豐富或容易獲取時(shí),支持向量機(jī)的優(yōu)勢(shì)更加明顯,所以,它得到了學(xué)者的青睞。支持向量機(jī)的泛化能力相對(duì)其它的模型更好,本文提出了基于支持向量機(jī)的集成模型�；诖髷�(shù)據(jù)時(shí)代背景下對(duì)互聯(lián)網(wǎng)金融個(gè)人信用數(shù)據(jù)進(jìn)行評(píng)估,本文在數(shù)據(jù)分析與整合方面進(jìn)行探索分析。本文在現(xiàn)有研究的基礎(chǔ)上,提出了基于支持向量機(jī)的集成模型RSBC-SVM,它以支持向量機(jī)作為基學(xué)習(xí)器,結(jié)合了bagging和random subspace兩種常見(jiàn)的集成策略以及相關(guān)性最小化集成選擇方法。此外,它還使用了模式搜索算法進(jìn)行參數(shù)優(yōu)化。RSBC-SVM模型的構(gòu)建分四個(gè)階段。第一個(gè)階段為數(shù)據(jù)分割,該階段先把原始數(shù)據(jù)分成初始訓(xùn)練集、驗(yàn)證集和測(cè)試集等三部分。本文使用訓(xùn)練集的數(shù)據(jù)訓(xùn)練個(gè)體學(xué)習(xí)器,使用驗(yàn)證集的數(shù)據(jù)挑選個(gè)體學(xué)習(xí)器,使用測(cè)試集的數(shù)據(jù)對(duì)所構(gòu)建的集成模型進(jìn)行效果驗(yàn)證。初始訓(xùn)練集經(jīng)過(guò)bagging和random subspace算法處理后又產(chǎn)生若干個(gè)新的訓(xùn)練子集。第二個(gè)階段為個(gè)體學(xué)習(xí)器的訓(xùn)練,在每一個(gè)新的訓(xùn)練子集上構(gòu)建相應(yīng)的支持向量機(jī)模型,并采用模式搜索算法調(diào)參。從個(gè)體學(xué)習(xí)器的角度分析,運(yùn)用模式搜索算法尋找參數(shù),提高了個(gè)體學(xué)習(xí)器的泛化能力;從個(gè)體學(xué)習(xí)器之間的關(guān)系角度分析,模式搜索算法為每一個(gè)支持向量機(jī)模型匹配不同參數(shù),增強(qiáng)了個(gè)體學(xué)習(xí)器的多樣性。第三個(gè)階段為個(gè)體學(xué)習(xí)器的選擇,本文采用相關(guān)性最小化方法對(duì)集成模型進(jìn)行修剪,減小集成規(guī)模有助于減小模型的存儲(chǔ)開(kāi)銷和預(yù)測(cè)開(kāi)銷,而且增強(qiáng)了個(gè)體學(xué)習(xí)器間的差異性。第四個(gè)階段為合成模型,此階段為RSBC-SVM模型構(gòu)建的最后一步,本階段先用Sigmoid函數(shù)將支持向量機(jī)的決策值輸出轉(zhuǎn)換成概率輸出,而后使用簡(jiǎn)單平均法對(duì)個(gè)體學(xué)習(xí)器進(jìn)行組合。本文最后還嘗試在互聯(lián)網(wǎng)金融個(gè)人信用數(shù)據(jù)上對(duì)所構(gòu)建的RSBC-SVM模型進(jìn)行效果驗(yàn)證。在數(shù)據(jù)實(shí)驗(yàn)前需要對(duì)數(shù)據(jù)進(jìn)行預(yù)處理,本文使用隨機(jī)森林方法插補(bǔ)缺失值,箱線圖法刪除異常數(shù)據(jù),使用對(duì)數(shù)變換和歸一化方法對(duì)變量進(jìn)行處理。最后,與其它五種模型進(jìn)行了對(duì)比分析,研究表明本文所構(gòu)造的模型性能最好,具有較強(qiáng)的現(xiàn)實(shí)意義。本文的理論創(chuàng)新點(diǎn)在于對(duì)支持向量機(jī)作了深入研究,提出了新的集成模型RSBC-SVM,豐富了支持向量機(jī)的理論研究。影響集成模型效果的因素之一是個(gè)體學(xué)習(xí)器間的差異性;個(gè)體學(xué)習(xí)器多樣性強(qiáng),集成模型的效果就越好。在增強(qiáng)個(gè)體學(xué)習(xí)器的多樣性方面,以往學(xué)者的關(guān)注重點(diǎn)是數(shù)據(jù)擾動(dòng)、特征擾動(dòng)和參數(shù)擾動(dòng),他們忽視了在合成模型前對(duì)個(gè)體學(xué)習(xí)器的選擇研究。在互聯(lián)網(wǎng)金融的背景下,本文采用了相關(guān)性最小化集成模型選擇方法對(duì)個(gè)體學(xué)習(xí)器進(jìn)行選擇,為集成模型的個(gè)體學(xué)習(xí)器的選擇研究提供了有益的參考。以上的研究,不僅在豐富支持向量機(jī)的內(nèi)容方面具有一定的理論意義,而且在推動(dòng)我國(guó)信用體系建設(shè),提高我國(guó)互聯(lián)網(wǎng)金融機(jī)構(gòu)消費(fèi)信貸市場(chǎng)的風(fēng)險(xiǎn)管理水平,促進(jìn)我國(guó)消費(fèi)信貸市場(chǎng)的進(jìn)一步發(fā)展方面具有一定的現(xiàn)實(shí)意義。
[Abstract]:The rapid development of China's economy has improved the capacity of residents' credit consumption. The rapid development of Internet finance has provided convenience for the residents' credit consumption, such as personal housing mortgage loans, personal small loans, credit card consumer loans and other credit products springing up. With the further credit of China's economy, credit consumption has stimulated the economy to increase. The long-term effect is further highlighted, and the willingness and ability of residents' credit consumption are rising steadily. The domestic Internet financial institutions have taken personal consumer loan business as one of the future development strategies. However, the risk management of personal consumer loans by internet financial institutions in China is relatively low, and the management means and methods are still relatively falling. In addition, there is no effective personal credit evaluation method for Internet financial institutions, which seriously hinders the development of personal credit business. An effective credit evaluation model can not only increase the profit of Internet financial institutions, but also expand the scale of credit of Internet financial institutions. Therefore, the research significance of personal credit evaluation method is heavy. In the era of Internet finance, the way of obtaining credit data has changed, not only from the traditional financial institutions to obtain credit data, but also from the e-commerce platform to obtain e-commerce data and to obtain social data from the social platform. Opportunities and challenges, if the lack of large data processing capacity, can not fully excavate the value hidden behind the mass credit data. The Internet financial institutions have used quantitative models to evaluate the consumer's personal credit risk. One of the focus of its research is the model of credit evaluation. Support vector machine is a data driven model, and it is a data driven model. In the process of supervised learning, data processing does not require a special assumption of data. When the amount of data is rich or easy to obtain, the advantage of support vector machine is more obvious. Therefore, it gets the favor of the scholars. The generalization ability of support vector machine is better than other models. This paper proposes an integrated model based on support vector machine. On the basis of the existing research, this paper proposes an integrated model RSBC-SVM based on support vector machine (SVM), which is based on support vector machine (SVM) as a base learner, combined with two kinds of bagging and random subspace. The common integration strategy and the correlation minimization integration selection method. In addition, it uses the pattern search algorithm to build the parameter optimization.RSBC-SVM model in four stages. The first phase is data segmentation, which first divides the original data into the initial training set, the validation set and the test set, and other three parts. This paper uses the training set. The data training individual learner, using the data of the verification set to select the individual learner, uses the data of the test set to verify the effect of the integrated model. After the initial training set is processed by bagging and random subspace algorithm, a number of new training subsets are produced. The second stage is the training of individual learner, in every one. A new subset of training subsets is constructed, and the model search algorithm is used to adjust the parameter. From the point of view of individual learner, the model search algorithm is used to search for parameters and improve the generalization ability of individual learner. From the angle of relationship between individual learners, the pattern search algorithm is each support vector machine. The model matches the different parameters and enhances the diversity of individual learner. The third stage is the choice of individual learner. This paper uses the correlation minimization method to trim the integrated model, reducing the size of the integration helps to reduce the storage overhead and the prediction overhead, and increases the difference between the individual learners. Fourth orders are enhanced. The stage is the synthetic model. This stage is the last step of building the RSBC-SVM model. In this stage, the Sigmoid function is used to convert the decision value output of support vector machine into probability output, and then the individual learner is combined with a simple mean method. Finally, this paper also tries to build the RSBC-SVM module on the Internet gold to integrate the personal credit data. It needs to preprocess the data before the data experiment. This paper uses the random forest method to interpolate the missing value, the box line graph method deletes the abnormal data, uses the logarithmic transformation and normalization method to deal with the variables. Finally, the comparison analysis is carried out with the other five models, and the research shows the model performance constructed in this paper. The theoretical innovation of this paper is to make a thorough study of the support vector machine, and put forward a new integrated model RSBC-SVM, which enriches the theoretical research of support vector machines. One of the factors that affect the effect of the integrated model is the difference between individual learners, the diversity of individual learners and the effectiveness of the integrated model. As for the diversity of the individual learner, the focus of previous scholars' attention is on data disturbance, characteristic disturbance and parameter disturbance. They ignore the selection of individual learners before the synthetic model. Under the background of Internet finance, this paper adopts the correlation minimization integration model selection method to individual learning. The selection of the device provides a useful reference for the selection of the individual learner of the integrated model. The above study not only has a certain theoretical significance in enriching the content of support vector machines, but also promotes the construction of the credit system in China, and improves the risk management level of the consumer credit market of the Internet financial institutions in China, and promotes the promotion of the risk management level of the consumer credit market of the Internet financial institutions in China. The further development of China's consumer credit market has certain practical significance.

【學(xué)位授予單位】：浙江財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F724.6;F832.4

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