基于支持向量机集成的个人信用评估研究

信用评估模型的优劣会对信贷机构损益和金融市场秩序产生直接的影响，为提升个人信用评估模型的精度，将集成方法应用到信用评估领域，提出改进DS证据理论的支持向量机集成个人信用评估模型，并将属性约减纳入建模过程中。利用C4.5决策树约减冗余属性，并根据数据集类别标签和支持向量机混淆矩阵，后验概率构造证据理论概率赋值函数。计算基于分类器间支持度的权重与专家权重修正由于训练过程受到干扰而产生的冲突证据。通过DS融合做出最终决策。实证分析探讨了该方法的优越性和可行性，可成为一种有效信用评估工具。     

最小二乘支持向量机在医疗数据分析中的应用

以医疗数据为应用对象,应用网格搜索和交叉验证的方法选择参数,建立最小二乘支持向量机分类器,进行实际验证,并与使用K近邻分类器(K-NN)和C4.5决策树两种方法的结果进行比较.结果表明,LS-SVM分类器取得较高的准确率,表明最小二乘支持向量机在医疗诊断研究中具有很大的应用潜力.     

基于随机森林的帕金森疾病诊断模型构建研究

近些年来,根据帕金森疾病(PD)患者的语音数据对该疾病做出诊断成为一种行之有效的疾病诊断方法。首先,针对语音数据集中存在非均衡数据和噪声样本的问题使用SVM SMOTE过采样技术,利用支持向量机分类器寻找支持向量并在此基础上合成新的样本以达到均衡数据集的目的;为了减少数据维度,降低学习难度,运用信息增益特征选择对所有特征属性计算数值并划分数据集以此来获得信息增益,根据信息增益的大小排序选取得到八个特征作为最优特征组合;最后,构建随机森林帕金森疾病诊断模型,并采用网格搜索和交叉验证相结合的方式进行参数调优,进一步优化模型,实现诊断模型准确率的进一步提高。实验结果表明,优化后模型的准确率、灵敏度和特异度均有提升,为别为96.59%、94.81%和95.49%,且准确率均高于支持向量机、最邻近节点算法、朴素贝叶斯和决策树等决策模型。     

基于改进GS-XGBoost的个人信用评估

信用评估分类器的好坏能够直接影响信贷金融机构的盈利能力. 传统的网格搜索法进行参数寻优时会耗费大量的时间, 基于此提出改进的网格搜索法优化XGBoost (GS-XGBoost)的个人信用评估算法. 该算法利用随机森林进行特征选择后, 将改进的网格搜索法对XGBoost中的n_estimators和learning_rate进行参数寻优, 建立评估模型. 从UCI数据库中选取信贷数据进行分析, 分别与支持向量机、随机森林、逻辑回归、神经网络以及未改进的XGBoost进行比较. 实验结果表明, 该模型的F-score和G-mean的值均有提高.     

针对小规模数据集的多模型融合算法研究

目前,对小规模数据集进行预测时,主要使用传统机器学习算法,但传统单一模型预测效果不能达到预期准确率,且无法兼顾多项评价指标。因此,文中以小规模数据集为研究对象,融合决策树、逻辑回归、支持向量机三类模型,提出了一种多模型融合算法,并分析了其在小规模数据集上的应用效果。首先,简述了决策树、逻辑回归和支持向量机的算法原理;其次,使用决策树、逻辑回归和支持向量机作为基学习器并完成单独训练,将各模型输出结果用于下一阶段模型输入,同时使用最大似然估计迭代优化参数,从而完成多模型融合过程;最后,对数据集进行分析和处理,通过实验与单一模型进行指标对比。实验结果表明,多模型融合算法在预测精确率、召回率、准确率等方面有明显提升。     

结合粗糙集与支持向量回归进行油藏物性参数预测

为了更准确的预测油藏物性3个重要参数:孔隙度,渗透率、饱和度,提出了结合粗糙集属性约简和支持向量机回归的方法.首先用粗糙集理论对测井数据样本属性进行约简,从而选出决策属性,构成新的样本数据.然后用支持向量回归理论对数据样本进行训练,建立支持向量回归模型,并且对测试样本进行预测.实验结果表明,该方法获得了较好的拟舍结果,并且减少了支持向量机在训练中的计算复杂度,提高了物性参数预测的准确率.执行该方法可为油藏开发提供决策依据.     

深度优先搜索的支持向量机参数优化算法

研究支持向量机参数优化问题,由于算法要求准确选择SVM参数,支持向量机在处理大样本数据集时和最优模型参数确定时,消耗的时间长、占有内存大,易获得局部最优解的难题.为了解决支持向量机存在的不足,采用深度优先搜索算法对其参数优化机机制进行改进.将向量机参数优化视成一个组合优化问题,将支持向量机模型的分类误差作为优化目标函数,采用深度优先算法对其进行求解,最后将模型应用于3个标准分类数据集.仿真结果表明,优化参数后的支持向量机加快模型的训练速度度,提高了分类的准确率,很好的解决了支持向量机参数优化难题.     

IA-SVM算法在网络入侵检测中的研究

研究网络入侵检测问题,网络入侵具有不确定性、多变性和动态性,传统检测方法不能很好的识别这种特性,且传统支持向量机参数采优化方法易出现参数选择不当,导致网络入侵检测准确率低.为了提高网络入侵检测准确率,将免疫算法引入到网络入侵检测中,用其优化支持向量机参数.方法将网络入侵检测数据输入到支持向量机中学习,将支持向量机参数作为免疫算法的抗体,把网络入侵检测准确率作为免疫算法抗原,通过抗体和抗原相互作用得到最优的支持向量机参数,然后对网络入侵数据检测得到入侵检测结果,最后通过DRAP网络入侵数据集对该方法进行仿真.仿真结果表明,相对传统网络入侵检测方法,新方法学习速度快,检测准确率高,很好地解决了传统检测方法准确率低的难题,为网络安全提供了保障.     

基于免疫遗传算法的支持向量机参数优化及其应用

支持向量机是在统计学习理论基础上发展起来的一种性能优良的新型机器学习方法,它具有坚实的理论基础,巧妙的算法实现。支持向量机的卓越性能依赖于它的参数的正确选择。本文采用改进的免疫遗传算法对支持向量机的参数进行优化。实验证明对于低维数据分类时,本文的优化算法比传统的网格法可以较大减少参数优化时间和提升分类的准确率。对高维的文本数据分类时,在保证分类准确率的前提下,仍然可以较大减少优化的时间。     

基于遗传算法的决策树优化模型

在分析C4.5算法原理的基础上,进一步讨论了C4.5算法在决策树的规模控制、属性选择、滤躁和去除不相关属性等方面的不足,讨论了决策树挖掘中对训练数据进行属性约简的必要性。从实用的角度提出了一种利用遗传算法进行寻优的、基于属性约简的决策树构建模型,并为此模型设计了一个适应度函数。该模型具有自适应的特点,通过调整适应度函数的参数,可以约束遗传算法的寻优方向,实现对决策树的优化。实验表明,决策树寻优后,在所用训练集属性减少的同时,分类精度却有一定程度的提高,而分类规则的规模却降低了,因此,该模型具有一定的实用价值。     

Credit scoring with a data mining approach based on support vector machines

The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.     

Credit scoring with a data mining approach based on support vector machines

The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.     

Neighborhood rough set and SVM based hybrid credit scoring classifier

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score according to the applicant’s input features: (1) using neighborhood rough set to select input features; (2) using grid search to optimize RBF kernel parameters; (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation; (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.     

基于选择性抽样的SVM增量学习算法的泛化性能研究

针对大数据环境中存在很多的冗余和噪声数据,造成存储耗费和学习精度差等问题,为有效的选取代表性样本,同时提高学习精度和降低训练时间,提出了一种基于选择性抽样的SVM增量学习算法,算法采用马氏抽样作为抽样方式,抽样过程中利用决策模型来计算样本间的转移概率,然后通过转移概率来决定是否接受样本作为训练数据,以达到选取代表性样本的目的。并与其他SVM增量学习算法做出比较,实验选取9个基准数据集,采用十倍交叉验证方式选取正则化参数,数值实验结果表明,该算法能在提高学习精度的同时,大幅度的减少抽样与训练总时间和支持向量总个数。     

面向征信数据安全共享的SVM训练机制

在征信行业中,征信数据的丰富性和多样性对信用评价极为重要.然而,征信机构尤其是小型征信机构拥有的征信数据存在内容不完整、种类不全、数量不充足等问题.同时由于征信数据价值高、隐私性强、易被非授权复制,征信机构之间难以直接共享数据.针对这一问题,本文提出了面向征信数据安全共享的SVM训练机制.首先共享数据经同态加密后存储在区块链上,保证数据不可篡改以及隐私安全.其次使用基于安全多方计算的支持向量机(SVM)在共享的加密数据上进行运算,保证在不泄露原始数据的条件下,训练信用评价模型.最后,通过真实数据集上的实验对本文所提出机制的可用性和性能进行验证.实验结果显示,相比于基于明文数据集训练出的模型,本文提出的机制在可接受时间内训练出的模型无准确率损失.同时,与其他同类隐私训练方案相比,本机制在实验数据集.上的计算耗时小于对比实验的5%,且无需可信第三方协助计算.     

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

Feature selection is a challenging task that has been the subject of a large amount of research, especially in relation to classification tasks. It permits to eliminate the redundant attributes and enhance the classification accuracy by keeping only the relevant attributes. In this paper, we propose a hybrid search method based on both harmony search algorithm (HSA) and stochastic local search (SLS) for feature selection in data classification. A novel probabilistic selection strategy is used in HSA–SLS to select the appropriate solutions to undergo stochastic local refinement, keeping a good compromise between exploration and exploitation. In addition, the HSA–SLS is combined with a support vector machine (SVM) classifier with optimized parameters. The proposed HSA–SLS method tries to find a subset of features that maximizes the classification accuracy rate of SVM. Experimental results show good performance in favor of our proposed method.     

Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

Microarray data classification is a task involving high dimensionality and small samples sizes. A common criterion to decide on the number of selected genes is maximizing the accuracy, which risks overfitting and usually selects more genes than actually needed. We propose, relaxing the maximum accuracy criterion, to select the combination of attribute selection and classification algorithm that using less attributes has an accuracy not statistically significantly worst that the best. Also we give some advice to choose a suitable combination of attribute selection and classifying algorithms for a good accuracy when using a low number of gene expressions. We used some well known attribute selection methods (FCBF, ReliefF and SVM-RFE, plus a Random selection, used as a base line technique) and classifying techniques (Naive Bayes, 3 Nearest Neighbor and SVM with linear kernel) applied to 30 data sets involving different cancer types.     

An artificial immune classifier for credit scoring analysis

The primary concern of the rating policies for a banking industry is to develop a more objective, accurate and competitive scoring model to avoid losses from potential bad debt. This study proposes an artificial immune classifier based on the artificial immune network (named AINE-based classifier) to evaluate the applicants’ credit scores. Two experimental credit datasets are used to show the accuracy rate of the artificial immune classifier. The ten-fold cross-validation method is applied to evaluate the performance of the classifier. The classifier is compared with other data mining techniques. Experimental results show that for the AINE-based classifier in credit scoring is more competitive than the SVM and hybrid SVM-based classifiers, except the BPN classifier. We further compare our classifier with other three AIS-based classifiers in the benchmark datasets, and show that the AINE-based classifier can rival the AIRS-based classifiers and outperforms the SAIS classifier when the number of attributes and classes increase. Our classifier can provide the credit card issuer with accurate and valuable information of credit scoring analyses to avoid making incorrect decisions that result in the loss of applicants’ bad debt.     

Mining the customer credit using hybrid support vector machine technique

Credit scoring has become a critical and challenging management science issue, as the credit industry has been facing fiercer competition in recent years. Many methods have been suggested to tackle this problem in the literature. In this paper, we proposed hybrid support vector machine technique based on three strategies: (1) using CART to select input features, (2) using MARS to select input features, (3) using grid search to optimize model parameters. In order to verify the feasibility and effectiveness of the proposed hybrid SVM model, one credit card dataset provided by a local bank in China is used in this study. Analytic results demonstrate that the hybrid SVM technique not only has the best classification rate, but also has the lowest Type II error in comparison with CART, MARS and SVM and justify the presumptions that SVM having better capability of capturing nonlinear relationship among variables.     

Using partial least squares and support vector machines for bankruptcy prediction

The evaluation of corporate financial distress has attracted significant global attention as a result of the increasing number of worldwide corporate failures. There is an immediate and compelling need for more effective financial distress prediction models. This paper presents a novel method to predict bankruptcy. The proposed method combines the partial least squares (PLS) based feature selection with support vector machine (SVM) for information fusion. PLS can successfully identify the complex nonlinearity and correlations among the financial indicators. The experimental results demonstrate its superior predictive ability. On the one hand, the proposed model can select the most relevant financial indicators to predict bankruptcy and at the same time identify the role of each variable in the prediction process. On the other hand, the proposed model’s high levels of prediction accuracy can translate into benefits to financial organizations through such activities as credit approval, and loan portfolio and security management.