本文選題：貝葉斯網(wǎng)絡 + 支持向量機��； 參考：《遼寧工程技術(shù)大學》2017年碩士論文

【摘要】：受數(shù)據(jù)收集條件所限,加上數(shù)據(jù)錄入時出現(xiàn)數(shù)據(jù)遺漏等情況,銀行客戶信用評估中經(jīng)常會出現(xiàn)數(shù)據(jù)嚴重缺失。這些數(shù)據(jù)的缺失會對銀行客戶信用評估造成極大影響。對缺失值的處理常用方法有刪除含缺失數(shù)據(jù)的記錄或者對缺失數(shù)據(jù)進行填充等方法,顯然,缺失數(shù)據(jù)填充將為未來信用評估奠定基礎。本文建立基于貝葉斯網(wǎng)絡與概率推理相結(jié)合的缺失數(shù)據(jù)填充方法。根據(jù)已有數(shù)據(jù)信息,挖掘?qū)傩灾g的相關(guān)性,建立貝葉斯網(wǎng)絡,由于貝葉斯網(wǎng)絡的特性可以簡化多維聯(lián)合概率的求解。根據(jù)概率推理的結(jié)果對缺失數(shù)據(jù)進行填充,推理結(jié)果還能反映出數(shù)據(jù)填充的精度,即概率越高,數(shù)據(jù)填充的準確率越高。在利用貝葉斯網(wǎng)絡推理方法處理缺失數(shù)據(jù)的基礎上,建立基于支持向量機的客戶信用評估體系,該體系包含三階段的信用評估模型,即是否違約的判定模型、違約概率的計算模型以及客戶信用風險度的預測模型。信用評估體系的建立,拓寬了客戶信用評估的輻射面,可以更加全面的對客戶信用進行評估,為銀行提供決策依據(jù),降低銀行的不良信貸率,具有重要的理論以及現(xiàn)實意義。
[Abstract]:Limited by the condition of data collection and the omission of data in data entry, the bank customer credit evaluation often has serious data deficiency. The lack of these data will have a great impact on the credit evaluation of bank customers. The common methods to deal with missing values are to delete records containing missing data or fill in missing data. Obviously, the filling of missing data will lay a foundation for future credit evaluation. In this paper, the missing data filling method based on Bayesian network and probabilistic reasoning is established. Based on the existing data information, the correlation between attributes is mined, and Bayesian networks are established. Because of the characteristics of Bayesian networks, the solution of multidimensional joint probability can be simplified. According to the result of probabilistic reasoning, the missing data is filled, and the result can reflect the precision of data filling, that is, the higher the probability, the higher the accuracy of data filling. On the basis of using Bayesian network reasoning method to deal with missing data, a customer credit evaluation system based on support vector machine (SVM) is established. The system consists of a three-stage credit evaluation model, that is, the judgment model of default or non-default. The calculation model of default probability and the prediction model of customer credit risk. The establishment of credit evaluation system broadens the radiation surface of customer credit evaluation, which can evaluate customer credit more comprehensively, provide decision basis for banks, and reduce the bad credit rate of banks, which has important theoretical and practical significance.
【學位授予單位】：遼寧工程技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F274;TP18

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