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.     

Public Displays of Play: Studying Online Games in Physical Settings

As research on virtual worlds gains increasing attention in educational, commercial, and military domains, a consideration of how player populations are ‘reassembled’ through social scientific data is a timely matter for communication scholars. This paper describes a large‐scale study of virtual worlds in which participants were recruited at public gaming events, as opposed to through online means, and explores the dynamic relationships between players and contexts of play that this approach makes visible. Challenging conventional approaches to quantitatively driven virtual worlds research, which categorizes players based on their involvement in an online game at a particular point in time, this account demonstrates how players' networked gaming activities are contingent on who they are playing with, where, and when.     

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。     

格值一阶逻辑LF(X)中的α-语义归结方法

自动推理是人工智能的一个重要研究方向,基于归结原理的自动推理因易于在计算机上实现而得到广泛研究。语义归结是对归结原理的一种改进,它利用限制参与归结子句类型和归结文字顺序的方法来提高推理效率。为了提高基于格蕴涵代数的格值逻辑的α-归结原理的效率,将语义归结策略应用于α-归结原理。首先给出了格值一阶逻辑系统中的α-语义归结概念和α-语义归结演绎概念,接着讨论了格值一阶逻辑系统的α-语义归结方法,并证明了其可靠性和条件完备性,最后通过实例说明了其有效性。     

一阶谓词逻辑的图形推理法

针对已有一阶谓词逻辑推理方法中存在的推理效率低等问题,研究一种基于谓词/变迁系统的图形推理法。定义了描述谓词间与/或关系的谓词-与/或图,借助谓词-与/或图表示谓词/变迁系统,提出一种实现反向推理的目标制导的图形推理法。该方法推理效率高,较已有的推理方法具有一定的优越性。     

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。     

基本主方向关系的反关系推理

在深入研究了基于MBR的主方向关系的反关系推理的基础上,提出了一种基于区域对象本身的基本主方向关系的反关系推理算法。在理论上对该算法的正确性和完备性进行了证明,并通过与实际情形逐一对比验证了该算法的正确性。     

Composing Cardinal Direction Relations Based on Interval Algebra

Direction relations between extended spatial objects are important commonsense knowledge. Skiadopoulos proposed a formal model for representing direction relations between compound regions (the finite union of simple regions), known as SK-model. It perhaps is currently one of most cognitive plausible models for qualitative direction information, and has attracted interests from artificial intelligence and geographic information system. Originating from Allen first using composition table to process time interval constraints; composing has become the key technique in qualitative spatial reasoning to check the consistency. Due to the massive number of basic directions in SK-model, its composition becomes extraordinary complex. This paper proposed a novel algorithm for the composition. Basing the concepts of smallest rectangular directions and its original directions, it transforms the composition of basic cardinal direction relations into the composition of interval relations corresponding to Allen''s interval algebra. Comparing with existing methods, this algorithm has quite good dimensional extendibility, that is, it can be easily transferred to the tridimensional space with a few modifications.     

二型模糊集模糊推理方法的单调性

模糊推理是蓬勃发展中的模糊控制技术的数学核心,二型模糊集的模糊推理是研究二型模糊逻辑系统的基础。各种模糊推理方法都具有单调性,从而导致人们当使用这些模糊推理方法时,应首先判明所讨论问题领域的推理是否具有相应的性质,否则可能导致不恰当的推理结果。     

基于二维多规则云模型定性推理的场地分类方法

针对场地土及其分类的模糊性和随机性特征,建立了基于二维多规则云模型定性推理的场地分类方法,实现了场地分类边界不确定性划分.设计了场地分类的二维多规则定性推理生成器,将其应用于场地类别和特征周期的识别过程中,实现了场地分类和特征周期的连续化,克服了在分类边界处易出现类别跳跃等不合理现象,基本实现了现行建(构)筑物抗震规范在场地分类方面的统一.最后利用该方法对74个实例进行分析,场地分类的正确率达到97.3%,表明了该方法是可行的、有效的.