本文關(guān)鍵詞：基于差異性度量的多分類器融合個人信用評估研究　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 個人信用評估 差異性 D-S證據(jù)理論 融合 集成

【摘要】：隨著我國商業(yè)銀行風(fēng)險管理的要求進(jìn)一步加強(qiáng)，個人消費信貸業(yè)務(wù)中的個人信用評估體系不斷優(yōu)化提升以保證商業(yè)銀行風(fēng)險管理更高的要求。在理論上各種單一模型不斷被優(yōu)化，無論是從數(shù)據(jù)集的收集、最優(yōu)特征提取還是從單一模型的頂層設(shè)計不斷的完善，都已經(jīng)到達(dá)了一定的高度，尤其是在大數(shù)據(jù)的呼聲越來越高的背景下，數(shù)據(jù)挖掘技術(shù)日益成熟，個人信用評估問題也逐步成為其中的一類十分具體的要求解決的實際問題，并且從大量的技術(shù)中選擇恰當(dāng)?shù)睦碚搧砬泻蟼�人信用評估問題的實際核心點成為日益迫切需要解決的問題，雖然大量的個人信用評估模型被構(gòu)建，但其中能夠?qū)崿F(xiàn)絕對優(yōu)勢的模型仍未被發(fā)現(xiàn)，尤其是理論上的模型如何能夠應(yīng)用于實際仍是一大問題。隨著單一模型的不斷優(yōu)化準(zhǔn)確率不斷提升及多分類器融合系統(tǒng)的發(fā)展，本研究在此基礎(chǔ)上進(jìn)行了更進(jìn)一步的研究以期能夠?qū)崿F(xiàn)評估模型準(zhǔn)確率和穩(wěn)健性的進(jìn)一步提升。 本研究對基于差異性度量多分類器融合的個人信用評估問題進(jìn)行了系統(tǒng)的研究，本研究是在個人信用評估單一的模型數(shù)量規(guī)模不斷增大，且單一模型不斷優(yōu)化的基礎(chǔ)上進(jìn)行，同時，也伴隨著多分類器融合問題逐步趨于成熟的背景而開展，基于這兩點趨勢發(fā)展，本文提出了基于差異性度量進(jìn)行多分類器融合以提高個人信用評估模型的準(zhǔn)確率和穩(wěn)健性，規(guī)避商業(yè)銀行風(fēng)險管理當(dāng)中個人信貸違約給銀行帶來的損失。本研究首先對差異性度量證據(jù)理論融合的個人信用評估模型進(jìn)行設(shè)計，從證據(jù)理論融合模型構(gòu)建，差異性度量方法討論及具體思路分析幾個方面進(jìn)行研究；其次，，本文根據(jù)前文思路進(jìn)行了基于差異性度量的單一分類器的證據(jù)理論融合，構(gòu)建了5種具有代表性的單一個人信用模型并利用4種差異性度量方法以對模型之間的互補(bǔ)性進(jìn)行考察，選取其中差異性最大的組合進(jìn)行證據(jù)理論融合，由結(jié)果看出，未全部發(fā)揮出雙方模型的優(yōu)勢，之后本研究利用準(zhǔn)確率和穩(wěn)健性對差異性度量的證據(jù)理論融合模型做進(jìn)一步優(yōu)化，結(jié)果表明這種綜合的優(yōu)化實現(xiàn)了模型準(zhǔn)確率和穩(wěn)健性的提升；再次，為實現(xiàn)單一模型的優(yōu)化并排除樣本結(jié)構(gòu)對于準(zhǔn)確率和差異性之間相關(guān)關(guān)系問題的考察，本文構(gòu)建了基于集成的個人信用評估模型，并對集成模型進(jìn)行了差異性度量和基于證據(jù)理論的融合，最后對集成模型的準(zhǔn)確率和差異性之間的相關(guān)關(guān)系進(jìn)行了實證分析，并期望實證結(jié)果能夠為后續(xù)模型優(yōu)化提供理論基礎(chǔ)和相應(yīng)的指導(dǎo)。
[Abstract]:With the requirement of risk management of commercial banks in our country further strengthened. Personal credit evaluation system in personal consumer credit business is constantly optimized to ensure higher requirements of commercial bank risk management. In theory, a variety of single models are constantly optimized, whether from the collection of data sets. Optimal feature extraction or continuous improvement from the top design of a single model has reached a certain height, especially in the context of big data's growing voice, data mining technology has become increasingly mature. Personal credit evaluation has gradually become one of the very specific practical problems to be solved. And choosing the appropriate theory from a large number of technologies to meet the actual core of personal credit evaluation has become an increasingly urgent problem, although a large number of personal credit evaluation models have been constructed. However, the model that can achieve absolute superiority has not been found. In particular, how the theoretical model can be applied in practice is still a big problem. With the continuous improvement of the accuracy of single model optimization and the development of multi-classifier fusion system. On this basis, further research is carried out to improve the accuracy and robustness of the evaluation model. This study systematically studies the problem of personal credit evaluation based on diversity measurement multi-classifier fusion. In this study, the number of individual credit assessment models is increasing. And the single model is continuously optimized on the basis of, at the same time, along with the multi-classifier fusion problem gradually mature background, based on these two trends to develop. In order to improve the accuracy and robustness of personal credit evaluation model, this paper proposes a multi-classifier fusion based on diversity measurement. In order to avoid the loss caused by individual credit default in the risk management of commercial banks, this study first designs the personal credit evaluation model which integrates the evidence theory of difference measurement, and constructs the model of evidence theory fusion. The difference measure method discussion and the concrete train of thought analysis several aspects carries on the research; Secondly, this paper carries on the evidence theory fusion of the single classifier based on the difference measure according to the previous ideas. In this paper, five representative single person credit models are constructed, and four different measures are used to investigate the complementarities between the models, and the most different combinations are selected for evidence theory fusion. From the results, not all of the advantages of the two models, after this study using accuracy and robustness to measure the evidence theory of diversity fusion model to further optimize. The results show that the model accuracy and robustness can be improved by the synthesis optimization. Thirdly, in order to optimize the single model and eliminate the sample structure for the accuracy and diversity of the correlation between the problem, this paper builds an integrated personal credit evaluation model. Finally, the correlation between the accuracy and the difference of the integration model is analyzed empirically. The empirical results are expected to provide theoretical basis and corresponding guidance for subsequent model optimization.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F830.5

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