The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

案例教学法在计算机网络课程教学中的应用

针对计算机网络课程的理论性、实践性和抽象性等特点,提出计算机网络课程案例教学法的可行性方案及其应用。通过TCP/IP协议族分层的原因和IP地址存在的原因和作用这两个具体的教学案例,开展具体的案例教学活动。分析表明,案例教学法在计算机网络课程教学中非常有效,应该进行推广和实施。     

中职计算机案例教学法的运用探讨

就案例教学法的相关概念,以及运用措施进行简单的总结和分析,并结合实际教学内容加以说明,以期使教学案例法能够更好的在中职计算机教学中得到应用,从而不断增强学生的计算机素质和能力,提高中职计算机课程的教学效率和质量。     

Polynomial time approximation algorithms for proportionate open‐shop scheduling

This paper deals with the presentation of polynomial time (approximation) algorithms for a variant of open‐shop scheduling, where the processing times are only machine‐dependent. This variant of scheduling is called proportionate scheduling and its applications are used in many real‐world environments. This paper develops three polynomial time algorithms for the problem. First, we present a polynomial time algorithm that solves the problem optimally if , where n and m denote the numbers of jobs and machines, respectively. If, on the other hand, , we develop a polynomial time approximation algorithm with a worst‐case performance ratio of that improves the bound existing for general open‐shops. Next, in the case of , we take into account the problem under consideration as a master problem and convert it into a simpler secondary approximation problem. Furthermore, we formulate both the master and secondary problems, and compare their complexity sizes. We finally present another polynomial time algorithm that provides optimal solution for a special case of the problem where .     

基于本体的语义虚拟环境查询与推理模型

当前将本体引入到语义虚拟环境的研究,只是将领域本体的可视化信息用本体表示,并未发挥本体本身具有的优势。为此,提出一种基于本体的语义虚拟环境查询与推理模型。利用OWL语言统一描述虚拟场景图形内容与语义信息,并分别对两者进行查询,在图形内容查询过程中引入本体的推理方法推理出隐含的图形内容信息,然后查询需要的信息。在语义信息查询时引入语义搜索方法,利用基于语义距离计算本体概念相似度的方法计算语义虚拟环境本体中类之间的相似度,搜索与被查询实例语义相似度最大的实例,并借助推理找出其间的关系。对语义虚拟家具商店进行本体的查询与推理,结果证明了该模型的可行性。     

语言真值直觉模糊逻辑的知识推理

针对格蕴涵代数、直觉模糊集及知识表示、基于语言真值直觉模糊代数的相关性质及运算方法,提出了六元语言真值直觉模糊代数的相关逻辑性质,并在六元语言真值直觉模糊知识表示的基础上,将模糊推理的CRI方法进行扩展,研究得出了六元语言真值直觉模糊推理的方法即6LTV-CRI算法。而后将直觉模糊推理与六元语言真值直觉模糊推理方法进行对比分析,验证了6LTV-CRI推理算法的合理性,并分析了其优缺点。     

基于约束关系的变强度组合测试用例集生成算法

针对传统的变强度组合测试方法对系统中各因素之间的约束关系考虑不足,从而可能导致测试用例冗余的问题,论文提出了一种基于one-Test-at-a-time策略的变强度组合测试用例集生成算法,该算法充分考虑了因素间的约束关系,从而在一定程度上减少了测试用例集的规模。最后通过实验验证了算法的可行性和有效性。     

使用程序静态分析技术识别Web程序中的用例结构

本文利用程序静态分析技术对Web程序中的用例识别进行了深入研究。对Web页面的用户行为进行了深入分析并建模,从行为模型中提取出描述整个系统中用户行为的事件流图,结合采用现有的从目的成功语句出发、以必经结点为基础的用例识别方法,从各个Web页面中提取的独立控制流图,形成全局控制流图,进行全局用例识别。与以前已有工作相比,本文考虑了Web程序的特点,尤其是前端程序如何实现用户行为,并在前端程序中进行精确的用例识别。本文采用目前使用较为广泛的JSP程序进行了实验,结果表明该方法能够有效识别JSP程序中的用例结构。     

“以人为本”理念支撑下高校ASP.NET课程教学体系构建

在新环境下,打破高等教育传统教学模式,建立以人为本的教学模式以适应时代的进步。ASP.NET是微软推出建立动态WEB应用程序的技术,在ASP.NET课程教学中深入以人为本的理念,以学生主体为出发点,展开教学内容安排、教学项目案例设计、教学策略及教学方法、考核方式等方面的研究,构建以人为本的立体化教学体系,供高校或培训机构参考。     

基于直觉模糊推理的网络舆情预警方法*

为解决网络舆情预警等级问题,提出了一种基于直觉模糊推理的网络舆情预警方法。借鉴战场态势分析思想,对网络舆情态势分析的原理进行了阐述,选取了适合计算机实现的七个网络舆情态势分析模式对预警等级进行判断。选择七个舆情话题进行实验,实验结果表明,该方法能够准确地估计出威胁等级,符合专家经验判断,说明该方法是可行的。