發(fā)布時間：2018-12-24 10:06

【摘要】：人們的生活與信用的關(guān)聯(lián)隨著社會的進(jìn)步及互聯(lián)網(wǎng)的發(fā)展日益密切。由于數(shù)據(jù)的分散、評估方法標(biāo)準(zhǔn)的不一,目前信用體系包括影響指標(biāo)體系、評估模型等還需要改善提高。論文針對信用數(shù)據(jù)指標(biāo)落后、數(shù)據(jù)分散不利于融合以及評估方法欠缺特殊情形考慮的問題,對信用體系進(jìn)行總結(jié)補充,將框架方法應(yīng)用于信用影響指標(biāo)體系的表示,并提出基于框架方法的信用評估方案。信用影響指標(biāo)隨著時代的進(jìn)步潛在地發(fā)生著變化,會出現(xiàn)更多的因素會對信用造成影響。論文從政府信用、企業(yè)信用、個人信用以及針對農(nóng)戶群體的農(nóng)戶信用這幾個方面分析補充了信用體系的可能性指標(biāo),并運用框架方法對其進(jìn)行表示。由于框架方法結(jié)構(gòu)清晰、便于擴展等優(yōu)勢,使得表示出的信用體系便于理解,且使用JSON格式直接存儲這樣的指標(biāo)結(jié)構(gòu)在內(nèi)容補充變化時不會帶來數(shù)據(jù)重構(gòu)產(chǎn)生的重復(fù)性工作。結(jié)合Logistic回歸模型對于各指標(biāo)權(quán)重的訓(xùn)練結(jié)果,運用框架方法解決問題的一般思路,改進(jìn)了匹配推理的原始方法。運用相似度計算進(jìn)行實例匹配,且用概率差替換屬性差進(jìn)行加權(quán)平均。由于將數(shù)據(jù)轉(zhuǎn)化為概率,因此,在用Logistic回歸建模時,將特征項數(shù)據(jù)轉(zhuǎn)化為對應(yīng)的概率,以實現(xiàn)數(shù)據(jù)的一致性。通過將該方案與DT、SVM進(jìn)行實驗比較,表明了該方案在信用評估方面的可行性,且在數(shù)據(jù)樣本較少時,提高了評估的準(zhǔn)確度,另外,由于該方案是在粗糙搜索后進(jìn)行實例匹配,極大地減少了時間的消耗。
[Abstract]:With the progress of society and the development of Internet, the relationship between people's life and credit becomes more and more close. Because of the dispersion of data and the different standards of evaluation methods, the current credit system, including the impact index system and the evaluation model, need to be improved and improved. Aiming at the problems that the credit data index is backward, the data dispersion is not conducive to the fusion and the evaluation method is not considered in the special situation, the paper summarizes and supplements the credit system, and applies the framework method to the expression of the credit impact index system. A scheme of credit evaluation based on frame method is proposed. With the progress of the times, more factors will influence the credit. In this paper, the possibility index of credit system is analyzed from the aspects of government credit, enterprise credit, personal credit and peasant household credit aiming at peasant household group, and the frame method is used to express it. Because the framework structure is clear and easy to extend, the expressed credit system is easy to understand, and the JSON format is used to store the index structure directly. Combined with the training result of Logistic regression model for each index weight, the original method of matching reasoning is improved by using the general idea of frame method to solve the problem. The similarity calculation is used for case matching, and the probability difference is used to replace the attribute difference for weighted average. Because the data is transformed into probability, when modeling with Logistic regression, the feature term data is transformed into the corresponding probability in order to realize the consistency of the data. By comparing the scheme with DT,SVM, it is shown that the scheme is feasible in credit evaluation, and the accuracy of evaluation is improved when the data sample is small. In addition, because the scheme is a case matching after rough search, The consumption of time has been greatly reduced.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O212.1

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