本文選題：信用評估 + 集成剪枝�。� 參考：《南京航空航天大學(xué)》2016年碩士論文

【摘要】：近年來,由于評估的準(zhǔn)確性嚴(yán)重影響到金融機(jī)構(gòu)的損失大小,信用評估問題得到了金融機(jī)構(gòu)越來越多的關(guān)注。目前學(xué)者們已經(jīng)提出很多方法用于解決信用評估問題。這些方法概括起來主要分為兩大類:基于統(tǒng)計學(xué)的方法和基于非統(tǒng)計學(xué)的方法,前者主要包括線性判別分析、logit分析和probit分析等,后者主要包括支持向量機(jī)(support vector machine,SVM)、人工神經(jīng)網(wǎng)絡(luò)(artificial neural network,ANN)和決策樹(decision tree,DT)等。盡管研究表明基于非統(tǒng)計學(xué)的方法取得了比較好的分類性能,但是單一模型用于解決信用評估問題存在著一定的局限性,后來學(xué)者們又提出了用集成學(xué)習(xí)(ensemble learning)解決該問題。然而,集成學(xué)習(xí)需要多個基分類器,這樣增加了時間和空間復(fù)雜性,同時泛化能力差的基分類器也影響著集成系統(tǒng)最終的分類效果。然而在原始的集成系統(tǒng)中選擇一個子集用于構(gòu)建集成系統(tǒng)可以很好的解決這個問題,這種方法被命名為選擇性集成(ensemble selection),或者也可以稱之為集成剪枝(ensemble pruning)。本文提出了ELMsGraspEnS算法用于解決信用評估問題。該算法用ELM作為生成集成系統(tǒng)的基學(xué)習(xí)機(jī),GraspEnS作為集成剪枝算法在原始的集成系統(tǒng)中選擇一部分子集,因此ELMsGraspEnS繼承了ELM和GraspEnS的優(yōu)點(diǎn)。ELM算法的學(xué)習(xí)速度非�？�,具有優(yōu)越的泛化性能,并且可以有效的解決局部最優(yōu)和過擬合問題。GraspEn S算法是GRASP算法在集成剪枝方面的應(yīng)用,是一種組合優(yōu)化的啟發(fā)式算法,不僅具有了貪婪集成剪枝算法的優(yōu)點(diǎn),并且可以避免貪婪集成剪枝算法所具有的局部最優(yōu)問題,另外,該算法還可以實(shí)現(xiàn)多點(diǎn)開始搜索。實(shí)驗(yàn)部分也表明了新提出的ELMsGraspEn S算法具有很好的分類效果。然而GRASP算法是一個無記憶算法,即在GRASP算法的迭代過程中無法利用前面迭代的信息,Path-Relinking算法是一個加強(qiáng)算法,融合GRASP和Path-Relinking可以避免GRASP算法中所存在的問題。鑒于此,本文提出了另一種PRelinkGrasp EnS算法用于解決信用風(fēng)險評估問題,該算法也是用ELM算法作為基學(xué)習(xí)機(jī),所不同的是在生成原始的集成系統(tǒng)時,用了Bagging技術(shù),這樣增加了基分類器的多樣性,該算法用融合了GRASP和Path-Relinking用于選擇性集成,這樣不僅具有GRASP算法的優(yōu)點(diǎn),也結(jié)合了Path-Relinking的優(yōu)勢,使得PRelinkGraspEnS算法是一個有記憶的算法,實(shí)驗(yàn)結(jié)果表明新提出的PRelinkGraspEn S算法不僅具有優(yōu)越的泛化性能,還可以加快收斂速度。
[Abstract]:In recent years, due to the accuracy of the evaluation of financial institutions seriously affected the size of losses, credit evaluation has been more and more concerned by financial institutions. At present, scholars have put forward many methods to solve the credit evaluation problem. These methods are divided into two main categories: statistical based method and non-statistical method. The former includes linear discriminant analysis (LDA) logit analysis and probit analysis. The latter includes support vector machine (support vector machine), artificial neural network (artificial neural network Ann) and decision tree (decision tree). Although the research shows that the non-statistical method achieves good classification performance, the single model has some limitations in solving the credit evaluation problem. Later, scholars proposed to use integrated learning (ensemble learning) to solve the problem. However, ensemble learning requires multiple base classifiers, which increase the complexity of time and space, and the poor generalization of base classifiers also affects the final classification effect of the ensemble system. However, choosing a subset from the original integration system to build the integration system is a good solution to this problem, which is called selective integration (ensemble selection), or integration pruning (ensemble pruning). In this paper, ELMsGraspEns algorithm is proposed to solve the credit evaluation problem. The algorithm uses ELM as the basic learning machine for generating integration system GraspEnS as the integrated pruning algorithm to select a subset in the original integration system, so ELMsGraspEnS inherits the advantages of ELM and GraspEnS. The learning speed of ELM algorithm is very fast. GraspEn S algorithm is an application of grasp algorithm in integrated pruning, and it is a heuristic algorithm of combinatorial optimization, which has excellent generalization performance and can effectively solve the problem of local optimality and over-fitting .GraspEn S algorithm is the application of grasp algorithm in integrated pruning, and it is a kind of heuristic algorithm of combinatorial optimization. It not only has the advantages of greedy integration pruning algorithm, but also avoids the local optimal problem of greedy integration pruning algorithm. In addition, the algorithm can also realize multi-point search. The experimental results also show that the new ELMsGraspEn S algorithm has a good classification effect. However, grasp algorithm is a memoryless algorithm. In the iterative process of grasp algorithm, the information of the previous iteration can not be used in the process of the Path-Relation algorithm is an enhanced algorithm, the fusion of grip and Path-Relationing algorithm can avoid the problems in grip algorithm. In view of this, this paper proposes another PRelinkGrasp Ens algorithm to solve the credit risk assessment problem. The algorithm also uses ELM algorithm as the basic learning machine. The difference is that bagging technique is used to generate the original integrated system. In this way, the diversity of base classifiers is increased. The algorithm combines grasp and Path-Relationing for selective integration, which not only has the advantages of grasp algorithm, but also combines the advantages of Path-Relationing, which makes PRelinkGraspEns algorithm a memorized algorithm. The experimental results show that the proposed PRelinkGraspEn S algorithm not only has excellent generalization performance, but also can accelerate the convergence speed.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP181
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本文編號：2101105