商业智能BI在银行信贷决策中的应用

根据银行业多年来的信贷记录,运用决策树算法进行建模、分类,从而构造出一棵决策树来帮助银行决策。决策树具有自学习能力,随着业务经验的积累,模型的再训练,其决策精度将会不断提高。     

基于多尺度粗糙集模型的决策树优化算法

针对经典决策树算法构造的决策树结构复杂、缺乏对噪声数据适应能力等局限性,基于多尺度粗糙集模型提出一种新的决策树构造算法。算法引入尺度变量和尺度函数概念,采用不同尺度下近似分类精度选择测试属性构造决策树,使用抑制因子对决策树进行修剪,有效地去除了噪声规则。结果表明该算法构造的决策树简单有效,对噪声数据有一定的抗干扰性,且能满足不同用户对决策精度的要求。     

变精度粗集模型在决策树生成过程中的应用

Pawlak粗集模型所描述的分类是完全精确的，而没有某种程度上的近似。在利用Pawlak粗集模型构造决策树的过程中，生成方法会将少数特殊实例特化出来，使生成的决策树过于庞大，从而降低了决策树对未来数据的预测和分类能力。利用变精度粗集模型，对基于Pawlak粗集模型的决策树生成方法进行改进，提出变精度明确区的概念，允许在构造决策树的过程中划入明确区的实例类别存在一定的不一致性，可简化生成的决策树，提高决策树的泛化能力。     

基于均值聚类和决策树算法的学生成绩分析

 针对高职学生的学习情况,采用k-均值聚类算法对学生的考试成绩进行等级划分,再采用R-C4.5算法构造决策树,通过对该决策树提取规则来分析学生各学科成绩和总评成绩的相关性。该方法可以减少决策树中的无意义的分支,挖掘出影响学生总评成绩的主要因素,为任课教师和教学管理人员在制定教学计划、开展教学工作和进行教学评价等方面提供参考。     

基于粗糙集的决策树构造算法

针对ID3算法构造决策树复杂、分类效率不高问题,基于粗糙集理论提出一种决策树构造算法。该算法采用加权分类粗糙度作为节点选择属性的启发函数,与信息增益相比,能全面地刻画属性分类的综合贡献能力,并且计算简单。为消除噪声对选择属性和生成叶节点的影响,利用变精度粗糙集模型对该算法进行优化。实验结果表明,该算法构造的决策树在规模与分类效率上均优于ID3算法。     

基于决策树理论的数据挖掘模型

提出了一个基于决策树理论的数据挖掘模型，该模型是数据挖掘中对样本进行分类的一种有效方法，它通过采用分级的形式，可以使复杂的分类问题逐步得到解决。在应用模型进行决策分析时,用给定的训练集构造一棵性能良好的决策树，然后选取合适的决策原则得出结论。在本文的最后给出了模型应用于交通领域的一个例子，说明如何在实际中运用该数据挖掘模型。     

基于变精度粗糙集的决策树构造改进算法

针对决策树构造中存在的最优属性选择困难、抗噪声能力差等问题,提出了一种新的基于变精度粗糙集模型的决策树构造算法.该算法采用近似分类精度作为节点选择属性的启发函数,与传统基于粗糙集的决策树构造算法相比,该算法构造的决策树结构简单,提高了决策树的泛化能力,同时对噪声也有一定的抑制能力.     

决策树算法在智能导学评价子系统中的应用

介绍智能导学系统的特点,并对决策树C4.5算法的原理进行了阐述,通过C4.5构造了一个学生在线学习效果的评估模型.并利用该模型得到的分类规则进行预测,得到准确性评估表,从而验证决策树算法的灵活性和计算的高效性.     

决策树算法在饰品营销中的应用

阐述了饰品企业营销的现状,提出了将数据挖掘技术应用到饰品营销中的方案.在分析决策树算法的基础上,介绍了决策树算法及决策树的构造,并使用该算法对企业客户进行分类及对新客户类型预测,实现对商业数据中隐藏信息的挖掘,且对该挖掘模型进行了验证.     

基于变精度粗糙集的决策树优化算法研究

应用变精度粗糙集理论，提出了一种利用新的启发式函数构造决策树的方法。该方法以变精度粗糙集的分类质量的量度作为信息函数，对条件属性进行选择。和ID3算法比较，本方法充分考虑了属性间的依赖性和冗余性，尤其考虑了训练数据中的噪声数据，允许在构造决策树的过程中划入正域的实例类别存在一定的不一致性，可简化生成的决策树，提高决策树的泛化能力。     

决策树算法在农户小额贷款中的应用研究

在讨论数据挖掘技术的基本概念、决策树方法的基础上,针对近年来农村信用社不良贷款的增加,提出了决策树算法在农户小额信用贷款评价中的应用。利用数据挖掘的预测功能,建立了一种较为科学明了,简单易行的农户信用评价模型,来应用于农村信用社对农户信用的评分,以作为贷款与否的依据。     

应用决策树方法构建评价指标体系

在根据不同应用改进信息熵计算方法的基础上，提出了利用信息增益选择属性作为评价指标并得到其权重的方法。使用信息增益生成决策树，给出利用决策树计算指标评分细则的方法。最后，通过个人住房贷款信用风险评估体系的建立验证了这些方法的实用性。     

决策树在贷款客户信用评估中的应用

针对如何在贷款过程中尽可能地降低风险,提出利用决策树理论对客户的基本情况进行分析,建立银行贷款客户信用评估决策树模型。为防止过拟合问题的产生,对最初生成的决策树又利用基于规则的方法进行裁剪修正,使得决策模型不只在训练集上的信用度决策获正确率较高,在相互独立的不同测试集和验证集上都取得令人满意的效果。     

人工神经网络在个人住房贷款信用评价中的应用

个人住房贷款在商业银行的业务中属于高风险的一种,因此,对贷款申请人进行信用评价极为重要。本文运用matlab软件,把人工神经网络与信用评价系统结合起来,贷款申请人的各项指标作为输入值,信用度为输出值,为科学评价个人信用提供了依据。     

Study of corporate credit risk prediction based on integrating boosting and random subspace

With the rapid growth and increased competition in credit industry, the corporate credit risk prediction is becoming more important for credit-granting institutions. In this paper, we propose an integrated ensemble approach, called RS-Boosting, which is based on two popular ensemble strategies, i.e., boosting and random subspace, for corporate credit risk prediction. As there are two different factors encouraging diversity in RS-Boosting, it would be advantageous to get better performance. Two corporate credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed method. Experimental results reveal that RS-Boosting gets the best performance among seven methods, i.e., logistic regression analysis (LRA), decision tree (DT), artificial neural network (ANN), bagging, boosting and random subspace. All these results illustrate that RS-Boosting can be used as an alternative method for corporate credit risk prediction.     

Neural network for predicting the performance of credit card accounts

This paper reports the interim results of an experimental project using neural networks as a decision support tool for credit card risk assessment within a major bank. Two prototype neural network systems have been developed: one which emulates the decisions of the current risk assessment system, and another which attempts to predict the performance of credit card accounts based on the accounts historical data. This paper focuses on the development of the neural network model for credit card account performance prediction. The study has shown that such a tool can help in discovering the potential problems with credit card applicants at the very early stage of the credit account life cycle.     

基于改进的代价敏感决策树的网络贷款分类

在网络贷款用户数据集中，贷款成功和贷款失败的用户数量存在着严重的不平衡，传统的机器学习算法在解决该类问题时注重整体分类正确率，导致贷款成功用户的预测精度较低。针对此问题，在代价敏感决策树敏感函数的计算中加入类分布，以减弱正负样本数量对误分类代价的影响，构建改进的代价敏感决策树；以该决策树作为基分类器并以分类准确度作为衡量标准选择表现较好的基分类器，将它们与最后阶段生成的分类器集成得到最终的分类器。实验结果表明，与已有的常用于解决此类问题的算法（如MetaCost算法、代价敏感决策树、AdaCost算法等）相比，改进的代价敏感决策树对网络贷款用户分类可以降低总体的误分类错误率，具有更强的泛化能力。     

A general model for fuzzy decision tree and fuzzy random forest

The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS‐based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS‐based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS‐based fuzzy decision tree with linear membership function and nonlinear membership function.     

基于语言共识模型的电子商务信用风险评价方法

电子商务信用风险评价能够更好地维护市场规则并防范交易主体的合法权益.从语言评价信息的角度,利用多属性群决策方法对电子商务信用风险评价方法进行探讨.首先,提出个体语言共识测度和群体语言共识测度;然后,针对共识性水平较低的决策群体,构建一种整数规划模型,用于调整决策者给出的初始语言决策信息;最后,提出一种基于语言共识模型的电子商务信用风险评价方法,并通过电子商务信用风险评价问题说明该方法的可行性和有效性.     

A credit ranking model for a parafinancial company based on the ELECTRE-III method and a multiobjective evolutionary algorithm

Credit rating is an assessment performed by lenders or financial institutions to determine a person’s creditworthiness based on the proposed terms of the loan. Frequently, these institutions use rating models to obtain estimates for the probabilities of default for their clients (companies, organizations, government, and individuals) and to assess the risk of credit portfolios. Numerous statistical and data mining methods are used to develop such models. In this paper, the potential of a multicriteria decision-aiding approach is studied. As a first step, the proposed methodology models the problem as a multicriteria evaluation process with multiple and in some cases, conflicting dimensions, which are integrated to derive sound recommendation for DMs. The second step of the methodology involves building a multicriteria outranking model based on ELECTRE III method. An evolutionary algorithm is used to exploit the outranking model. The methodology is applied to a small-scale financial institution operating in the agricultural sector. We compare loan applications based on their attributes and the credit profile of the customer or credit applicant. Our methodology offers the flexibility of combining heterogeneous information together with the preferences of decision makers (DMs), generating both relative and fixed rules for selecting the best loan applications among new and existing customers, which is an improvement over traditional methods The results reveal that outranking models are well suited to credit rating, providing good ranking results and suitable understanding on the relative importance of the evaluation criteria.