A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.     

Using neural network ensembles for bankruptcy prediction and credit scoring

Bankruptcy prediction and credit scoring have long been regarded as critical topics and have been studied extensively in the accounting and finance literature. Artificial intelligence and machine learning techniques have been used to solve these financial decision-making problems. The multilayer perceptron (MLP) network trained by the back-propagation learning algorithm is the mostly used technique for financial decision-making problems. In addition, it is usually superior to other traditional statistical models. Recent studies suggest combining multiple classifiers (or classifier ensembles) should be better than single classifiers. However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood. In this paper, we investigate the performance of a single classifier as the baseline classifier to compare with multiple classifiers and diversified multiple classifiers by using neural networks based on three datasets. By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets. The diversified multiple classifiers trained by not only different classifier parameters but also different sets of training data perform worse in all datasets. However, for the Type I and Type II errors, there is no exact winner. We suggest that it is better to consider these three classifier architectures to make the optimal financial decision.     

多层前馈神经网络在基于案例推理的应用

基于案例的推理（CBR）系统的增量式学习会使案例库逐渐增大,导致案例的检索时间较长,效率较低。多层前馈神经网络是构造性神经网络技术,很容易构筑及理解,具有较低的时间和空间复杂性和较高的识别率。利用该神经网络技术对案例库进行分类后,待求解的新问题只需在某个子案例库中进行检索,便可以有效地解决大规模案例库的能力与效率的维护问题,确保CBR系统的能力保护与效率保护兼顾的实现,为大规模案例库的应用提供技术保证。     

A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networks model. To demonstrate the effectiveness and feasibility of the proposed modeling procedure, credit scoring tasks are performed on one bank housing loan dataset using cross-validation approach. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an alternative in handling credit scoring tasks.     

基于粗糙集和神经网络集成的贷款风险5级分类

建立了粗糙集与神经网络集成的贷款风险5级分类评价模型,该模型首先利用自组织映射神经网络离散化财务数据并应用遗传算法约简评价指标;基于最小约简指标提取贷款风险5级分类判别规则以及对BP神经网络进行训练;最后使用粗糙集理论判别与规则库匹配的检验样本风险等级,使用神经网络判别不与规则库任何规则匹配的检验样本风险等级.利用贷款企业数据库698家5级分类样本进行实证研究,结果表明,粗糙集与神经网络集成的判别模型预测准确率达到82.07%,是一种有效的贷款风险5级分类评价工具.     

粗糙BP网络在葡萄病害分类中的应用

粗糙集无需提供问题所需处理的数据集合之外的任何先验信息,是一种通过知识约简,消除冗余数据的软计算方法;BP神经网络是一种通过自身的学习机制自动形成所要求的决策区域技术.综合了粗糙集和BP神经网络的各自优势,构建了一种新颖的葡萄病害分类模型.测试结果表明,所建模型对葡萄病害分类是行之有效的.     

A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model

Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this study, we select important variables by genetic algorithm (GA) to combine the bank’s internal behavioral scoring model with the external credit bureau scoring model to construct the dual scoring model for credit risk management of mortgage accounts. It undergoes more accurate risk judgment and segmentation to further discover the parts which are required to be enhanced in management or control from mortgage portfolio. The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model. Moreover, this study proposes credit strategies such as on-lending retaining and collection actions for corresponding customers in order to contribute benefits to the practice of banking credit.     

带权重的RBF神经网络银行个人信用评价方法

针对RBF神经网络确定核函数中心时没有考虑输入样本分类指标权重的问题,提出了一种动态加权聚类算法.在算法中利用样本之间的加权距离代替了欧氏距离作为选定核函数中心的量度.在此基础上,建立了信用评价模型,利用已知类别的样本对模型进行训练,再利用训练好的模型对未知类别的样本进行预测,实验结果验证了模型的有效性.     

粗糙集和神经网络方法在数据挖掘中的应用

提出了一种基于神经网络和粗集的数据挖掘新方法。首先利用粗集理论对原始数据进行一致性属性约简,然后使用神经网络对数据进行学习,并同时完成属性的不一致约简,最后再由粗集对神经网络中的知识进行规则抽取。该方法充分融合了粗集理论强大的属性约简、规则生成能力和神经网络优良的分类、容错能力。实验表明,该方法快速有效,生成规则简单准确,具有良好的鲁棒性。     

An application of locally linear model tree algorithm with combination of feature selection in credit scoring

Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets – Australian and German – from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.     

A new approach for automated parts recognition using time series analysisand neural networks

This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.     

Metaheuristic algorithm to train product and sigmoid neural network classifiers

This paper develops three frameworks based on a metaheuristic algorithm to train neural network classifiers. The architecture is a single‐hidden‐layer feedforward network. The first methodology spreads a base configuration over the nodes of a computing cluster; each of them executes the same algorithm to train the neural network with a different parameter setting. The second approach does a refined training via a biphase metaheuristic algorithm to maintain the diversity a period longer than the usual; it may be run in a sequential or distributed way. The third framework performs a data preparation phase by means of feature subset selection to reduce the number of inputs to the biphase metaheuristic algorithm. The two first methodologies have been tested using a complete test bed with product and unipolar sigmoid units in the hidden layer, and the statistical tests reveal that product nodes are significantly the most accurate. The third framework has included four feature subset selectors with different properties to reduce the number of inputs to the product unit artificial neural network, and the nonstatistical test shed light on that the results with a preprocessing phase are significantly more accurate than the results with the raw data.     

基于信用机制的网络用户管理方法研究

网络用户管理是网络管理的重点也是难点,为了进一步提高网络管理的稳定性和可靠性,在分析网络用户上网行为的基础上,提出基于信用机制的网络用户管理方法.以金融领域较为成熟的信用模型对网络用户行为进行信用评估,利用信用值对网络用户进行管理.实验结果表明,利用信用模型的网络管理方法,减轻了网络管理员工作负担,并且提高了网络的稳定性和网络用户管理的有效性,该方法具有良好的鲁棒性和较强的适应能力,为网络管理提供一种新思路.     

A neural network approach for defect identification and classification on leather fabric

In this paper, an automated vision system is presented to detect and classify surface defects on leather fabric. Visual defects in a gray-level image are located through thresholding and morphological processing, and their geometric information is immediately reported. Three input feature sets are proposed and tested to find the best set to characterize five types of defects: lines, holes, stains, wears, and knots. Two multilayered perceptron models with one and two hidden layers are tested for the classification of defects. If multiple line defects are identified on a given image as a result of classification, a line combination test is conducted to check if they are parts of larger line defects. Experimental results on 140 defect samples show that two-layered perceptrons are better than three-layered perceptrons for this problem. The classification results of this neural network approach are compared with those of a decision tree approach. The comparison shows that the neural network classifier provides better classification accuracy despite longer training times.     

自适应增强卷积神经网络图像识别

目的 为了进一步提高卷积神经网络的收敛性能和识别精度,增强泛化能力,提出一种自适应增强卷积神经网络图像识别算法。方法 构建自适应增强模型,分析卷积神经网络分类识别过程中误差产生的原因和误差反馈模式,针对分类误差进行有目的地训练,实现分类特征基于迭代次数和识别结果的自适应增强以及卷积神经网络权值的优化调整。自适应增强卷积神经网络与多种算法在收敛速度和识别精度等性能上进行对比,并在多种数据集上检测自适应卷积神经网络的泛化能力。结果 通过对比实验可知,自适应增强卷积神经网络算法可以在很大程度上优化收敛效果,提高收敛速度和识别精度,收敛时在手写数字数据集上的误识率可降低20.93%,在手写字母和高光谱图像数据集上的误识率可降低11.82%和15.12%;与不同卷积神经网络优化算法对比,误识率比动态自适应池化算法和双重优化算法最多可降低58.29%和43.50%;基于不同梯度算法的优化,误识率最多可降低33.11%;与不同的图像识别算法对比,识别率也有较大程度提高。结论 实验结果表明,自适应增强卷积神经网络算法可以实现分类特征的自适应增强,对收敛性能和识别精度有较大的提高,对多种数据集有较强的泛化能力。这种自适应增强模型可以进一步推广到其他与卷积神经网络相关的深度学习算法中。     

基于人工神经网络对sEMG信号的手势动作识别

针对利用表面肌电信号(sEMG)对手势动作的肌电信号的研究较少和sEMG信号处理过于复杂的问题,提出了利用人工神经网络和sEMG信号对人的手势动作进行识别研究,引入了MYO硬件设备对新的手势动作sEMG信号采集.利用MYO从手臂上获取每一个手势动作的sEMG信号,提取信号特征值,作为算法的训练数据和测试数据.采用人工神经网络中的反向传递神经网络算法来进行对4种不同手势动作分类,对应目标手指识别率在90.35%.研究结果可以被用来做临床诊断和生物医学的应用以及用于现代硬件的发展和更现代化的人机交互的发展.     

Artificial neural network approach in printed circuit board assembly

In this study, automation of the circuit board assembly process is considered using artificial neural networks with knowledge-based systems. Basic issues in achieving intelligent control that can adapt to changing conditions in the assembly process are discussed. The feasibility of using neural networks for pattern recognition and optimum component insertion sequence generation is examined. The study provides a basic foundation for designing a conceptual architecture for adaptive intelligent control of circuit board assembly. Real-time testing of component recognition is conducted using adaptive resonance theory (ART 1) as a neural network paradigm.     

Content based audio classification: a neural network approach

Content based music genre classification is a key component for next generation multimedia search agents. This paper introduces an audio classification technique based on audio content analysis. Artificial Neural Networks (ANNs), specifically multi-layered perceptrons (MLPs) are implemented to perform the classification task. Windowed audio files of finite length are analyzed to generate multiple feature sets which are used as input vectors to a parallel neural architecture that performs the classification. This paper examines a combination of linear predictive coding (LPC), mel frequency cepstrum coefficients (MFCCs), Haar Wavelet, Daubechies Wavelet and Symlet coefficients as feature sets for the proposed audio classifier. Parallel to MLP, a Gaussian radial basis function (GRBF) based ANN is also implemented and analyzed. The obtained prediction accuracy of 87.3% in determining the audio genres claims the efficiency of the proposed architecture. The ANN prediction values are processed by a rule based inference engine (IE) that presents the final decision.     

Decision tree-based technology credit scoring for start-up firms: Korean case

Various types of Technology Credit Guarantees (TCGs) have been issued to support technology development of start-up firms. Technology evaluation has become a critical part of TCG system. However, general technology credit scoring models have not been applied reflecting the special phenomena of start-ups, which are distinguishable from those of established firms. Furthermore, somewhat complicated approaches have been applied to existing models. We propose a rather simple decision tree-based technology credit scoring for start-ups which can serve as a-replacement for the complicated models currently used for general purposes. Our result is expected to provide valuable information to evaluator for start-up firms.     

高分辨卫星图像卷积神经网络分类模型

目的 卫星图像往往目标、背景复杂而且带有噪声,因此使用人工选取的特征进行卫星图像的分类就变得十分困难。提出一种新的使用卷积神经网络进行卫星图像分类的方案。使用卷积神经网络可以提取卫星图像的高层特征,进而提高卫星图像分类的识别率。方法 首先,提出一个包含六类图像的新的卫星图像数据集来解决卷积神经网络的有标签训练样本不足的问题。其次,使用了一种直接训练卷积神经网络模型和3种预训练卷积神经网络模型来进行卫星图像分类。直接训练模型直接在文章提出的数据集上进行训练,预训练模型先在ILSVRC（the ImageNet large scale visual recognition challenge）-2012数据集上进行预训练,然后在提出的卫星图像数据集上进行微调训练。完成微调的模型用于卫星图像分类。结果 提出的微调预训练卷积神经网络深层模型具有最高的分类正确率。在提出的数据集上,深层卷积神经网络模型达到了99.50%的识别率。在数据集UC Merced Land Use上,深层卷积神经网络模型达到了96.44%的识别率。结论 本文提出的数据集具有一般性和代表性,使用的深层卷积神经网络模型具有很强的特征提取能力和分类能力,且是一种端到端的分类模型,不需要堆叠其他模型或分类器。在高分辨卫星图像的分类上,本文模型和对比模型相比取得了更有说服力的结果。