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.     

基于SVM的局部潜在语义分析算法研究

针对现有的Web文本分类与表示方法中出现的各种分类效果与性能优化等问题,基于局部潜在语义分析的理论原理,利用支持向量机分类优势,设计出一种基于文档与类别之间相关度的生成局部区域的算法,即S-LLSA。该算法在语义分析使用矩阵的奇异值分解过程中引入不同类别信息,分析特征词的局部特征,使用支持向量机分类器计算文本对类别的相关度参数,并应用于局部区域生成过程。通过实验表明,S-LLSA算法有效解决了局部区域如何进行局部奇异值分解问题,有效提高并优化了Web文本分类效果,更好地表示了Web文本潜在语义空间。     

Credit scoring by feature-weighted support vector machines

Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics.In this paper,a novel feature-weighted support vector machine(SVM) credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance ranking.Considering the mutual interaction among modeling features,random forest is further introduced for relative feature importance measurement.These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.     

基于几何算法的投影孪生支持向量机

投影孪生支持向量机（PTSVM）是最近提出的一种具有较好泛化性能的分类模型,但由于采用内点算法求解二次规划问题,PTSVM的训练速度较慢。针对该缺陷,提出一种快速的、基于几何算法的 PTSVM（GPTS-VM）。遵循 PTSVM的几何思想,提出一种新的二次规划模型,为每类数据产生一个投影方向;然后基于优化理论推导该模型的对偶问题并给予明确的几何解释,并利用计算几何算法求解。实验表明,提出的方法具有更快的训练速度和更好的泛化性能。     

基于支持向量机的图像型火灾探测算法

针对传统火灾探测方法存在的不足,提出了一种基于支持向量机的图像型火灾探测算法,并与基于神经网络的图像型火灾探测算法做了比较。实验结果表明支持向量机克服了神经网络容易过学习、容易陷入局部极小点等不足,同时避免了人为设定特征量识别阈值时需要做大量实验和统计的复杂性。基于支持向量机的图像型火灾探测算法识别准确率高,对于小样本、高维数、非线性的分类问题效果显著。     

Orthogonal support vector machine for credit scoring

The most commonly used techniques for credit scoring is logistic regression, and more recent research has proposed that the support vector machine is a more effective method. However, both logistic regression and support vector machine suffers from curse of dimension. In this paper, we introduce a new way to address this problem which is defined as orthogonal dimension reduction. We discuss the related properties of this method in detail and test it against other common statistical approaches—principal component analysis and hybridizing logistic regression to better solve and evaluate the data. With experiments on German data set, there is also an interesting phenomenon with respect to the use of support vector machine, which we define as ‘Dimensional interference’, and discuss in general. Based on the results of cross-validation, it can be found that through the use of logistic regression filtering the dummy variables and orthogonal extracting feature, the support vector machine not only reduces complexity and accelerates convergence, but also achieves better performance.     

支持向量机理论及算法研究综述

介绍了SVM的理论基础和它的多种主要算法及这些算法的利弊与发展现状,并介绍了SVM在现实生活中的应用原理及应用现状。最后分析了SVM在发展中的不足之处,指出了其研究方向及前景,并提出在分布式支持向量机这个方向上可以进行更深层次的研究。     

基于梯度算法的支持向量机参数优化方法

首先介绍最近出现的参数优化方法,概括了高效率的参数优化算法应具备的若干特点.然后提出了一种新的支持向量机参数优化方法.该方法先在局域内用混沌优化搜索局域最优点,再将此最优点作为梯度方向,通过改变局域范围跳出局部寻优区域.该方法降低了对性能函数连续且可微的要求,收敛速度快,最终优化解与支持向量机的参数初始值无关.最后,通过仿真实验表明了该方法具有更高的分类和回归准确率.     

基于模糊支持向量机的多分类算法研究

针对支持向量机理论中的多分类问题以及SVM对噪声数据的敏感性问题,提出了一种基于二叉树的模糊支持向量机多分类算法。该算法是在基于二叉树的支持向量机多分类算法的基础上引入模糊隶属度函数,根据每个样本数据对分类结果的不同影响,通过基于KNN的模糊隶属度的度量方法计算出相应的值,由此得到不同的惩罚值,这样在构造分类超平面时,就可以忽略对分类结果不重要的数据。通过实验证明,该算法有较好的抗干扰能力和分类效果。     

基于支持向量机和遗传算法的人脸识别研究

采用自适应遗传算法(AGA)优化筛选改进高斯核函数支持向量机(SVM)参数模型进行人脸特征分类。支持向量机的泛化性能主要取决于核函数类型和核函数参数及惩罚系数C,本文在传统高斯核函数基础上提出改进高斯核函数作为支持向量机的非线性映射函数,并使用自适应遗传算法优化筛选核函数参数和支持向量机惩罚系数,将优化后的SVM模型用于人脸库进行实验仿真。实验结果表明,本文方法比传统高斯核函数支持向量机分类器模型有更高识别率。     

基于支持向量机的乳腺钙化点检测算法

针对乳腺X线图像微钙化点检测假阳性高的问题,提出一种微钙化点检测算法.算法首先以小波与Top-hat算子相结合的方法进行钙化点粗检测,然后以支持向量机(SVM:Support Vector Machine)为工具对粗检测结果进行真钙化点与假钙化点分类.对开放乳腺图像数据库MIAS的仿真实验表明,算法的检出率超过98%,错检率不足4%,达到理想的检测效果.     

基于pinball损失的结构模糊多分类支持向量机算法

针对多分类支持向量机（MSVM）对噪声较强的敏感性、对重采样数据的不稳定性以及泛化性能低等缺陷,将pinball损失函数、样本模糊隶属度以及样本结构信息引入到简化的多分类支持向量机（SimMSVM）算法中,构建了基于pinball损失的结构模糊多分类支持向量机算法Pin-SFSimMSVM。在人工数据集、UCI数据集以及添加不同比例噪声的UCI数据集上的实验结果显示：所提出的Pin-SFSimMSVM算法与SimMSVM算法相比,准确率均提升了0~5.25个百分点;所提出的算法不仅具有避免多类数据存在不可分区域和计算速度快的优点,而且具有对噪声较好的不敏感性以及对重采样数据的稳定性,同时考虑了不同数据样本在分类时扮演不同角色的事实以及数据中包含的重要先验知识,从而使分类器训练更准确。     

基于改进支持向量机的Wi-Fi室内定位算法

针对室内复杂环境下无线信号不稳定、传统支持向量机定位算法计算复杂度高等难题,为了提高室内的定位精度,提出一种改进支持向量机的Wi-Fi室内定位算法。采用核主成分分析对特征进行降维处理,提取有用信息、降低计算量,采用支持向量机构建定位特征与物理位置的非线性映射模型,并采用粒子群算法对模型参数进行优化,进行了仿真实验。结果表明,该算法提高了室内定位精度和效率。     

基于人工鱼群算法的孪生支持向量机

孪生支持向量机（twin support vector machine, TWSVM）是在支持向量机的基础上产生的机器学习算法,具有训练速度快、分类性能优越等优点。但是孪生支持向量机无法很好地处理参数选择问题,不合适的参数会降低分类能力。人工鱼群算法（artificial fish swarm algorithm, AFSA）是一种群智能优化算法,具有较强的全局寻优能力和并行处理能力。本文将孪生支持向量机与人工鱼群算法结合,来解决孪生支持向量机的参数选择问题。首先将孪生支持向量机的参数作为人工鱼的位置信息,同时将分类准确率作为目标函数,然后通过人工鱼的觅食、聚群、追尾和随机行为来更新位置和最优解,最后迭代结束时得到最优参数和最优分类准确率。该算法在训练过程中自动确定孪生支持向量机的参数,避免了参数选择的盲目性,提高了孪生支持向量机的分类性能。     

Credit rating with a monotonicity-constrained support vector machine model

Deciding whether borrowers can fulfill their obligations is a major issue for financial institutions, and while various credit rating models have been developed to help achieve this, they cannot reflect the domain knowledge of human experts. This paper proposes a new rating model based on a support vector machine with monotonicity constraints derived from the prior knowledge of financial experts. Experiments conducted on real-world data sets show that the proposed method, not only data driven but also domain knowledge oriented, can help correct the loss of monotonicity in data occurring during the collecting process, and performs better than the conventional counterpart.     

基于k均值聚类的直推式支持向量机学习算法

针对直推式支持向量机（TSVM）学习模型求解难度大的问题,提出了一种基于k均值聚类的直推式支持向量机学习算法——TSVMKMC。该算法利用k均值聚类算法,将无标签样本分为若干簇,对每一簇样本赋予相同的类别标签,将无标签样本和有标签样本合并进行直推式学习。由于TSVMKMC算法有效地降低了状态空间的规模,因此运行速度较传统算法有了很大的提高。实验结果表明,TSVMSC算法能够以较快的速度达到较高的分类准确率。     

支持向量机模型分类能力指标的统计分析

在支持向量机（support vector machines, SVM）中,如何衡量SVM的分类能力,最小化风险泛函是一个重要的指标。根据支持向量机小样本特点,给出了支持向量机分类能力的一个量化标准：最优超平面的可靠度β。详细讨论了β的下界和置信区间,并给出了在实际应用中,如何根据样本数据估计β的下界和置信区间。实验也证明了β的下界估计和置信区间的合理性、有效性。     

基于信息浓缩的隐私保护支持向量机分类算法

支持向量机(SVM)的分类决策过程涉及到对原始训练样本的学习,容易导致数据中隐私信息的泄漏。为解决上述问题,提出一种基于信息浓缩的隐私保护分类方法IC-SVM。该算法首先根据样本的邻域信息,通过模糊C均值(FCM)聚类算法进行聚类分析;接着,使用信息浓缩准则对聚类中心进行处理,得到浓缩点组成的新样本;最后,使用新样本进行训练并得到决策函数,并用它去进行分类测试,可以较好地保护数据的隐私。在UCI真实数据和PIE人脸数据上的实验结果表明,IC-SVM方法既能保护数据信息的安全,又有较高的分类准确率。     

群智能算法优化支持向量机参数综述

支持向量机建立在统计学习的理论基础之上,具有理论的完备性,但是在应用上仍然存在模型参数难以选择的问题。首先,介绍了支持向量机和群智能算法的基本概念;然后,系统地叙述了各种经典的群智能算法进行支持向量机参数优化取得的最新研究成果以及总结了优化过程中存在的问题和解决方案;最后,结合该领域当前研究现状,提出了群智能算法优化支持向量机参数研究中需要关注的问题,展望了这一研究方向在未来的发展趋势和前景。