【摘要】：隨著中國(guó)市場(chǎng)經(jīng)濟(jì)的高速發(fā)展,以個(gè)人的信用作為擔(dān)保向銀行貸款正在成為一種趨勢(shì)。信用貸款數(shù)量以及貸款金額的不斷增加,使得銀行面臨客戶(hù)違約所造成的風(fēng)險(xiǎn)也在逐年增加,因此,在向客戶(hù)發(fā)放信用貸款前,銀行必須根據(jù)客戶(hù)的真實(shí)信息客觀(guān)準(zhǔn)確地對(duì)其信用進(jìn)行評(píng)估,根據(jù)信用情況發(fā)放貸款,從而降低由于客戶(hù)違約而給銀行帶來(lái)的損失。本文分析了樣本數(shù)據(jù)集和平均一依賴(lài)估測(cè)(Averaged One-Dependence Estimators,AODE)模型結(jié)構(gòu)特點(diǎn),針對(duì)樣本數(shù)據(jù)集中的連續(xù)屬性,首先提出了一種離散化方法,連續(xù)屬性經(jīng)過(guò)離散化處理后,能夠有效地提高AODE模型的分類(lèi)精度;其次根據(jù)粗糙集理論的屬性約簡(jiǎn)規(guī)則,在保持信息系統(tǒng)分類(lèi)能力不變的條件下,刪除其中不相關(guān)或不重要的指標(biāo)屬性,從而篩選出能表示樣本的最小指標(biāo)集;最后將Adaboost與AODE模型相結(jié)合構(gòu)建集成AODE分類(lèi)器,構(gòu)建個(gè)人信用評(píng)估模型。本文主要工作如下:(1)為了解決樣本數(shù)據(jù)集中的連續(xù)屬性離散化問(wèn)題,提出一種改進(jìn)的的離散粒子群優(yōu)化算法。將連續(xù)屬性的斷點(diǎn)集合作為離散粒子群,通過(guò)粒子間的相互作用最小化斷點(diǎn)子集,同時(shí)引入模擬退火算法作為局部搜索策略,提高了粒子群的多樣性和尋找全局最優(yōu)解的能力;利用粗糙集理論中決策屬性對(duì)條件屬性的依賴(lài)度衡量決策表的一致性,從而達(dá)到連續(xù)屬性離散化的目的。(2)針對(duì)樣本數(shù)據(jù)集中大部分指標(biāo)屬性存在冗余且不具備同等重要性,不利于在數(shù)據(jù)分析中做出簡(jiǎn)明的決策,對(duì)樣本數(shù)據(jù)集的指標(biāo)屬性進(jìn)行約簡(jiǎn)是信用評(píng)估的重要步驟。本文提出一種基于禁忌離散粒子群優(yōu)化的屬性約簡(jiǎn)算法對(duì)個(gè)人信用評(píng)估指標(biāo)進(jìn)行選取。由于禁忌搜索算法對(duì)初始解有較強(qiáng)的依賴(lài)性,而離散粒子群算法在迭代時(shí)容易陷入局部最優(yōu)解,因此在指標(biāo)選取過(guò)程中,采用離散粒子群算法在全局進(jìn)行搜索,禁忌搜索算法在局部進(jìn)行尋優(yōu),在不影響分類(lèi)質(zhì)量的前提下,刪除冗余屬性,簡(jiǎn)化知識(shí)庫(kù),構(gòu)建個(gè)人信用評(píng)估指標(biāo)集合。(3)AODE模型在進(jìn)行分類(lèi)時(shí),組成它的每一個(gè)超父獨(dú)依賴(lài)估測(cè)模型(Super Parent One-Dependence Estimator,SPODE)對(duì)分類(lèi)的貢獻(xiàn)程度是一樣的,然而每一個(gè)SPODE模型的結(jié)構(gòu)不同,對(duì)最終分類(lèi)結(jié)果的影響也不同。本文針對(duì)平均一依賴(lài)估測(cè)算法的結(jié)構(gòu)弱點(diǎn)提出了相應(yīng)的改進(jìn)。首先從構(gòu)成它的每一個(gè)超父獨(dú)依賴(lài)估測(cè)模型中,采用隨機(jī)抽樣法,選取一定數(shù)量的SPODE模型組成平均一依賴(lài)估測(cè)模型;然后采用Adaboost算法構(gòu)建集成AODE分類(lèi)模型;最后將集成AODE分類(lèi)模型用于個(gè)人信用評(píng)估。仿真實(shí)驗(yàn)結(jié)果表明,集成AODE評(píng)估模型能夠有效地提高個(gè)人信用評(píng)估的預(yù)測(cè)準(zhǔn)確率。
[Abstract]:With the rapid development of China's market economy, it is becoming a trend to take personal credit as guarantee to lend to banks. The number of credit loans and the amount of loans are increasing, which makes banks face the risk of customers defaulting. Therefore, before issuing credit loans to customers, The bank must evaluate its credit objectively and accurately according to the customers' true information, and make loans according to the credit situation, so as to reduce the losses caused by the customers' default. In this paper, the structural characteristics of sample dataset and Averaged One-Dependence estimation (Aode) model are analyzed. For the continuous attributes of the sample dataset, a discretization method is proposed, and the continuous attributes are discretized. It can effectively improve the classification accuracy of AODE model. Secondly, according to the attribute reduction rules of rough set theory, the irrelevant or unimportant index attributes can be deleted under the condition that the classification ability of information system remains unchanged. Finally, the Adaboost and AODE model are combined to construct the integrated Aode classifier, and the personal credit evaluation model is constructed. The main work of this paper is as follows: (1) an improved discrete particle swarm optimization algorithm is proposed to solve the continuous attribute discretization problem in the sample data set. The breakpoint set of continuous attributes is regarded as discrete particle swarm, and the breakpoint subset is minimized by the interaction between particles. At the same time, simulated annealing algorithm is introduced as a local search strategy, which improves the diversity of particle swarm and the ability of finding global optimal solution. The consistency of decision table is measured by the dependence of decision attributes on conditional attributes in rough set theory, so as to achieve the purpose of discretization of continuous attributes. (2) aiming at the redundancy of most index attributes in the sample data set and the lack of equal importance, most of the index attributes in the sample data set are redundant and do not have the same importance. It is unfavorable to make simple decision in data analysis. Reducing index attribute of sample data set is an important step of credit evaluation. In this paper, an attribute reduction algorithm based on Tabu discrete Particle Swarm Optimization (DPSO) is proposed to select individual credit evaluation indexes. Because Tabu search algorithm has strong dependence on initial solution, and discrete particle swarm optimization algorithm is easy to fall into local optimal solution during iteration, discrete particle swarm optimization algorithm is used to search globally in the process of index selection. Tabu search algorithm searches locally, removes redundant attributes, simplifies knowledge base, and constructs individual credit evaluation index set without affecting classification quality. (3) AODE model is used for classification. Each superparent One-Dependence estimation model (SPODE) has the same contribution to the classification, but each SPODE model has different structure and different influence on the final classification results. In this paper, we propose a corresponding improvement to the structural weakness of the average-dependence estimation algorithm. First of all, a certain number of SPODE models are selected to form an average dependency estimation model from each of the super-parent sole dependence estimation models, and then the integrated AODE classification model is constructed by using Adaboost algorithm. Finally, the integrated AODE classification model is applied to personal credit assessment. The simulation results show that the integrated AODE evaluation model can effectively improve the prediction accuracy of personal credit assessment.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

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本文編號(hào)：2133911