本文選題：信用評分 + 案例檢索��； 參考：《中國軟科學(xué)》2014年12期

【摘要】：從樣本偏差和信用樣本動態(tài)變化問題出發(fā),以CBR(案例推理)方法建立個人信用評分模型。研究發(fā)現(xiàn),基于CBR的個人信用評分模型在案例檢索環(huán)節(jié)假設(shè)特征集中各特征變量具有相同權(quán)重,與個人信用評分實際不符;在案例修正環(huán)節(jié)假設(shè)相似案例權(quán)重相等,導(dǎo)致已有數(shù)據(jù)信息無法得到充分利用。針對這些局限性,本文設(shè)計了基于Logistic回歸-BP神經(jīng)網(wǎng)絡(luò)的權(quán)重調(diào)整算法,結(jié)合BP神經(jīng)網(wǎng)絡(luò)的高精度及Logistic回歸的穩(wěn)定性計算個人信用評分各特征變量的權(quán)重,對案例檢索進(jìn)行優(yōu)化;設(shè)計基于距離的投票算法計算各相似案例的權(quán)重,對案例修正進(jìn)行優(yōu)化。實證實驗證明基于優(yōu)化CBR的個人信用評分模型精確性和解釋性均有所提高,錯分率降低,能夠輸出各指標(biāo)的重要性,有效的利用已有數(shù)據(jù)信息,更加適用于個人信用評分。
[Abstract]:Based on the problem of sample deviation and dynamic change of credit sample, CBR (case based reasoning) method is used to establish personal credit scoring model. It is found that the CBR based personal credit scoring model assumes that each feature variable in the feature set has the same weight in the case retrieval link, which does not conform to the actual situation of the individual credit score; in the case revision link, the similar case weight is assumed to be equal. As a result, the existing data information can not be fully utilized. Aiming at these limitations, this paper designs a weight adjustment algorithm based on Logistic regression BP neural network, combines the high accuracy of BP neural network and the stability of Logistic regression to calculate the weight of individual credit score characteristic variables, and optimizes the case retrieval. A distance-based voting algorithm is designed to calculate the weights of similar cases and optimize the case correction. The empirical results show that the accuracy and explanation of the personal credit scoring model based on optimized CBR are improved, the error rate is reduced, the importance of each index can be outputted, and the existing data information is used effectively, which is more suitable for personal credit rating.
【作者單位】： 哈爾濱工業(yè)大學(xué)管理學(xué)院;
【基金】：國家自然基金項目(70871030) 黑龍江省自然科學(xué)基金項目(G200914)
【分類號】：F832.4

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本文編號：1831283