本文選題：個人信用評估 + Bagging模型�。� 參考：《南京信息工程大學(xué)》2016年碩士論文

【摘要】：伴隨著金融自由化及經(jīng)濟全球化進程的不斷深入,我國銀行業(yè)面臨著前所未有的挑戰(zhàn)。信用風(fēng)險管理的效率直接關(guān)系到商業(yè)銀行的經(jīng)營和發(fā)展,而研究和構(gòu)建更加科學(xué)、有效的個人信用評估方法,是當(dāng)前信用風(fēng)險管理研究的重大課題。目前,國內(nèi)外學(xué)者在個人信用評估方面進行了廣泛而深入的研究,設(shè)計了許多適應(yīng)特定金融環(huán)境的個人信用評估模型。本文首先對國內(nèi)外文獻從信用評估和集成方法兩方面進行梳理,論述了信用風(fēng)險的概念、成因、特征和個人信用評估的概念,并從傳統(tǒng)統(tǒng)計法和現(xiàn)代人工智能法兩方面介紹了信用風(fēng)險評估的方法和模型。在此基礎(chǔ)上,將Bagging集成分類法和各單一分類器應(yīng)用于兩組數(shù)據(jù)庫,利用德國和日本兩組數(shù)據(jù)庫檢驗?zāi)Ｐ偷姆诸惥群头�(wěn)健性。由實證結(jié)果看出,Bagging分類模型都具有較高的分類精度和穩(wěn)健性,相對單一分類模型,它的分類效果都不錯。本文還提出了改進的集成分類模型：Bagging-Bagging集成模型,結(jié)果顯示,Bagging-Bagging模型的分類效果更好,可以創(chuàng)新性的應(yīng)用于個人信用評估領(lǐng)域。同為集成方法的Adaboost模型,不論是模型對樣本的分類精度還是模型的穩(wěn)健性,都明顯不如Bagging-Bagging和Bagging-決策樹模型,更加驗證了Bagging集成模型的實用性。因此,可以認(rèn)為,Bagging集成模型相比其他模型,尤其是分類性能較弱的模型,有較強的提升分類性能的作用,更適合應(yīng)用于個人信用評估領(lǐng)域。
[Abstract]:With the deepening of financial liberalization and economic globalization, China's banking industry is facing unprecedented challenges. The efficiency of credit risk management is directly related to the management and development of commercial banks. At present, scholars at home and abroad have carried out extensive and in-depth research on personal credit assessment, and designed many personal credit evaluation models suitable for specific financial environment. This paper firstly combs the domestic and foreign literature from two aspects of credit evaluation and integration methods, discusses the concept, causes, characteristics and personal credit evaluation of the concept of credit risk. The methods and models of credit risk assessment are introduced from two aspects: traditional statistical method and modern artificial intelligence method. On this basis, the Bagging integrated taxonomy and each single classifier are applied to two groups of databases, and the classification accuracy and robustness of the model are verified by using the German and Japanese databases. The empirical results show that bagging classification models have high classification accuracy and robustness, compared with a single classification model, its classification effect is good. This paper also proposes an improved integrated classification model: Bagging-bagging model. The results show that the Bagging-bagging model has better classification effect and can be applied to personal credit assessment innovatively. Both the classification accuracy of the model and the robustness of the model are obviously lower than those of the Bagging-Bagging and the agginging-decision tree model, which verifies the practicability of the Bagging integration model. Therefore, compared with other models, especially those with weak classification performance, the bagging ensemble model has a stronger function of improving classification performance and is more suitable for personal credit evaluation.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：F224;F203
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本文編號：1974649