基于TimeRBM和项目属性聚类的混合协同过滤算法

将受限波尔茨曼机(Restricted Boltzmann Machine,RBM）用于协同过滤中的评分预测受到了不少学者的关注,但已有的方法一方面忽略了用户兴趣随时间变化的问题,另一方面只考虑了用户评分数据,而用户评分数据往往存在严重稀疏性问题,使得推荐性能较差。因此,本文首先提出了一种融合时间信息的基于项目的RBM模型：TimeRBM模型,通过在RBM模型中加入时间信息偏置项来进行改进模型;其次本文提出利用项目本身的属性进行聚类的方法进行评分预测;最后将TimeRBM模型得到的评分结果和项目属性聚类的评分结果进行加权融合得到一种评分预测的混合算法。在Movilens-100k数据集上进行实验,实验结果表明这种混合的算法有助于提高推荐系统的预测精度。     

基于聚类的个人信用评分模型研究

信用卡公司是一个服务性的金融企业,如何提高在服务过程中的服务质量,改进服务方法,使公司的决策更为准确及时,是信用卡公司追求的一个目标。本文介绍了神经网络方法及数据挖掘技术在信用卡公司对用户评分中的应用,对比分析了几种个人信用评分模型建模方法的特点,建立了一种决策树-神经网络个人信用评分模型,并针对该模型提出了一种近邻聚类算法,该算法在信用评分应用中可以得到较理想的结果。     

基于径向基神经网络的新型协同过滤推荐算法

针对传统协同过滤推荐算法对目标用户的评分预测过于依赖邻近用户,而忽略目标用户自身评分特性的问题,提出一种改进的基于径向基RBF(Radial Basis Function)神经网络的预测方法。该方法首先使用RBF神经网络对邻近用户的项目评分数据进行模型训练,得到基于该用户的网络评分模型;然后结合目标用户自身的评分进行计算,得到一个基于该模型的评分;最后结合所有邻近用户的模型评分预测出目标用户对目标项目的最终评分。改进后的算法既借鉴了用户之间的相似性,也考虑了目标用户自身的评分特性。实验结果表明,改进后的协同过滤推荐算法可以获得比传统算法更好的推荐效果。     

基于ASP数据库网络评分模型的设计与实现

介绍了一种基于ASP和数据库的评分模型,实现了在Internet上对多媒体课件的评分及统计管理.     

面向协同过滤推荐的新型混合评分函数

协同过滤技术在现代推荐系统中得到了广泛的应用,其基本思想是相似的用户会喜欢相似的物品。评分函数(Score Function,SF)是协同过滤推荐模型的一个关键技术,用于评估用户对物品的喜好程度。然而,目前常用的评分函数存在如下缺陷,即内积评分函数难以有效捕捉用户与用户以及物品与物品的相似度,而欧几里德距离度量函数由于几何空间限制降低了模型的表达能力。文中提出了一种融合内积相似度和欧几里德距离度量的新颖的混合评分函数,并从理论上分析了此混合评分函数的性质,证明它能有效弥补现有评分函数的不足。此外,新的混合评分函数是一项通用技术,适用于诸多现有的推荐模型(如SVD++,MF,NGCF,CML等),能够提高模型的推荐质量。最后,在6个公开数据集上进行了大量实验,验证了新混合评分函数的优越性能。     

基于终身机器学习的主题挖掘与评分预测联合模型

为充分利用历史知识,提高评分预测精度,基于终身机器学习(LML)机制提出一种同时挖掘用户评分和评论的推荐模型。在执行任务时积累知识并用于后续任务的训练,提高评分预测精度。在真实数据集上的实验结果表明,与无LML能力的模型相比,该模型预测评分的均方误差降低5.4‰,且随着知识的积累,误差不断降低,提高了主题词语分类的精度。     

基于权重攻击的联邦学习防御方案

为提高联邦学习中恶意模型检测的准确率和鲁棒性,提出一种基于权重攻击的联邦学习防御方案。基于局部离群因子算法设计异常检测模型,提出用于检测异常模型的异常评分;提出一种基于密度的异常检测算法计算每个局部模型的异常评分;利用异常评分在聚合过程中自适应调整每个局部模型的权值。仿真结果表明,所提方案检测恶意模型精准度有所提高,具有良好的收敛性和稳定性。     

基于孪生网络和BERT模型的主观题自动评分系统

由于现在缺乏多语言教学中的主观题自动评分, 针对这一问题提出了一种基于孪生网络和BERT模型的主观题自动评分系统. 主观题的问题文本和答案文本通过自然语言预处理BERT模型得到文本的句向量, BERT模型已经在大规模多种语言的语料上经过训练, 得到的文本向量包含了丰富的上下文语义信息, 并且能处理多种语言信息. 然后把...     

基于UBM的发音质量评价算法

将已经成功应用到说话人识别/确认领域中的高斯混合模型和全局背景模型(UBM)引入语音发音质量评价领域,提出一种新的评价英语发音质量的算法。该算法训练出标准发音的全局背景模型。UBM模型描述与音素无关的特征分布,定义段时长归一化的相似度比例对数为音素的发音质量分数,综合得到整句发音的评分结果。实验证明,在实验室自行采集的非母语语音数据库上,该算法评分与专家评分的相关性达到了0.700,优于其他评分算法。     

XML关键字检索系统的数据源选择

随着互联网数据爆炸式的增长,信息检索系统逐步采用分布式多数据源架构存储数据,在关键字检索时,选择与用户查询的关键字相关度大的数据源进行查询对提高检索效率显得格为重要.提出一种基于关键字检索的XML数据源选择方法,针对XML文档结构的层次特性,提出一种递归定义的结果评分模型,将结点的关键字频率与路径长度递归地加入到评分模型中,使得评分模型能够准确地评判结果的优劣.同时,利用评分模型定义并提取了XML数据源的摘要,并给出了摘要存储中涉及的压缩、优化、更新等问题的解决方案及算法.根据摘要信息,提出4种数据源选择的方案,并使用DBLP数据集来验证了文章所提出的数据源选择算法的有效性.     

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.     

Management of scoring sessions in alternative assessment: the computer-assisted scoring approach

We describe a computer-assisted scoring approach in educational assessment. In this approach, scores are captured and analyzed as scoring takes place; information on scoring quality is used to provide immediate feedback to raters and make timely re-calibration and dismissal decisions. We present a conceptual model for computer-assisted scoring and describe how we used this approach to manage the scoring sessions of an assessment for teacher certification. We found that computer-assisted scoring: (a) allowed us to provide immediate feedback to raters about their scoring quality and make accurate re-calibration and dismissal decisions; and (b) did not affect the dependability of the scores or the flow of the scoring sessions. We also confirmed that, with appropriate software, raters can be trained readily to score complex performance with the aid of computers even when they have no prior experience with computers. The conceptual model allowed us to identify how close our scoring sessions were to optimal efficiency.     

基于改进图半监督学习的个人信用评估方法

针对个人信用评估中未标号数据获取容易而已标号数据获取相对困难,以及普遍存在的数据不对称问题,提出了基于改进图半监督学习技术的个人信用评估模型。该模型采用了半监督学习技术,一方面能从大量的未标号数据中学习,避免了个人信用评估中已标号数据相对缺乏造成的泛化能力下降问题;另一方面,通过改进图半监督学习技术,对图半监督迭代结果进行归一化及修改决策边界,有效减小了数据不对称的影响。在UCI的三个信用审核数据集上的评测结果表明,该模型具有明显优于支持向量机和改进前方法的评估效果。     

Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method

With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classification techniques, genetic programming, support vector model etc. Computational time for running a model has a great importance in the 21st century. The algorithms or models with less computational time are more efficient and thus gives more profit to the banks or firms. In this study, we proposed a new strategy to reduce the computational time for credit scoring. In this approach we have used SVM incorporated with the concept of reduction of features using F score and taking a sample instead of taking the whole dataset to create the credit scoring model. We run our method two real dataset to see the performance of the new method. We have compared the result of the new method with the result obtained from other well known method. It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.     

Automated sleep stage scoring using hybrid rule- and case-based reasoning.

We propose an automated method for sleep stage scoring using hybrid rule- and case-based reasoning. The system first performs rule-based sleep stage scoring, according to the Rechtschaffen and Kale's sleep-scoring rule (1968), and then supplements the scoring with case-based reasoning. This method comprises signal processing unit, rule-based scoring unit, and case-based scoring unit. We applied this methodology to three recordings of normal sleep and three recordings of obstructive sleep apnea (OSA). Average agreement rate in normal recordings was 87.5% and case-based scoring enhanced the agreement rate by 5.6%. This architecture showed several advantages over the other analytical approaches in sleep scoring: high performance on sleep disordered recordings, the explanation facility, and the learning ability. The results suggest that combination of rule-based reasoning and case-based reasoning is promising for an automated sleep scoring and it is also considered to be a good model of the cognitive scoring process.     

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.     

BIC评分贝叶斯网络模型及其应用

针对入侵检测系统漏报率、误报率高的缺点,以贝叶斯信息标准(BIC)评分函数为尺度,结合爬山搜索算法,降低朴素贝叶斯网络模型的强独立性假设,提出更符合实际情形的BIC评分贝叶斯网络模型。对模型进行验证和性能分析,实验结果表明,基于BIC评分函数的贝叶斯网络模型对行为特征渐变的DoS攻击和刺探攻击具有较高识别率。     

基于Ext-GBDT集成的类别不平衡信用评分模型

针对现实信用评分业务中样本类别不平衡和代价敏感问题,以及金融机构更期望以得分的方式直观地认识贷款申请人的信用风险的实际需求,提出一种基于Ext-GBDT集成的类别不平衡信用评分模型。使用欠采样的方法从"好"客户(大类)中随机采样多份与全部"坏"客户(小类)等量的样本,分别与全部小类构成训练子集;用不同的训练子集及特征采样和参数扰动的方法训练得到多个差异化的Ext-GBDT子模型;然后使用简单平均法整合子模型的预测概率;最后将信用概率转换为信用评分。在UCI德国信用数据集上,以AUC和代价敏感错误率作为评价指标,与决策树、逻辑回归、朴素贝叶斯、支持向量机、随机森林及其集成模型等当前最为常用的信用评分模型进行对比,验证了该模型的有效性。     

Technology credit scoring model with fuzzy logistic regression

Technology credit scoring models have been used to screen loan applicant firms based on their technology. Typically a logistic regression model is employed to relate the probability of a loan default of the firms with several evaluation attributes associated with technology. However, these attributes are evaluated in linguistic expressions represented by fuzzy number. Besides, the possibility of loan default can be described in verbal terms as well. To handle these fuzzy input and output data, we proposed a fuzzy credit scoring model that can be applied to predict the default possibility of loan for a firm that is approved based on its technology. The method of fuzzy logistic regression as an appropriate prediction approach for credit scoring with fuzzy input and output was presented in this study. The performance of the model is improved compared to that of typical logistic regression. This study is expected to contribute to practical utilization of the technology credit scoring with linguistic evaluation attributes.     

Adaptive molecular docking method based on information entropy genetic algorithm

Almost all the molecule docking models, using by widespread docking software, are approximate. Approximation will make the scoring function inaccurate under some circumstances. This study proposed a new molecule docking scoring method: based on force-field scoring function, it use information entropy genetic algorithm to solve the docking problem. Empirical-based and knowledge-based scoring function are also considered in this method. Instead of simple combination with fixed weights, coefficients of each factor are adaptive in the process of searching optimum solution. Genetic algorithm with the multi-population evolution and entropy-based searching technique with narrowing down space is used to solve the optimization model for molecular docking problem. To evaluate this method, we carried out a numerical experiment with 134 protein–ligand complexes of the publicly available GOLD test set. The results show that this study improved the docking accuracy over the individual force-field scoring greatly. Comparing with other popular docking software, it has the best average Root-Mean-Square Deviation (RMSD). The average computing time of this study is also good among them.