Bankruptcy forecasting using case-based reasoning: The CRePERIE approach

Bankruptcy prediction is a very important research trend: although statistical methods are mainly used in literature, techniques based on Artificial Intelligence are interesting from many points of view. Among them, Case-Based Reasoning (CBR) could be useful to cluster enterprises according to opportune similarity metrics as well as suggest proper actions to take for avoiding bankruptcy in border-line situations. In this paper, we present a new and still under development CBR approach to this problem, that seems to return better results than previous attempts. The approach is based on different kinds of similarity metrics and is focused on the implementation of innovative revise algorithms. In particular, the paper shows how the revise step is crucial to improve the accuracy of the bankruptcy prediction model.     

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.     

A case-based reasoning approach to planning for disassembly

With recycling regulations, resource conservation needs and an increased awareness of the state of the environment by both the consumer and the producer, many companies are establishing disassembly plants and developing product designs that specifically facilitate disassembly. Once disassembled, the items can be reused, recycled or discarded. One can identify two distinct aspects of the disassembly problem: design for disassembly (DFD) and planning for disassembly (PFD). The goal of DFD is to design products that are easy to disassemble. On the other hand, the objective of PFD is to identify efficient sequences to disassemble products. This paper focuses on the PFD aspect of disassembly. Because there could be many ways to disassemble a given product, PFD knowledge is accumulated by experience. Such knowledge is valuable, and should be captured, saved and reused to solve similar problems that arise in the future. In this paper, we propose case-based reasoning (CBR) as an approach to solve PFD problems. CBR is based on the fundamental principle that problem solving can benefit from solutions to past problems that have been attempted. The technique and issues related to the application of CBR to PFD are presented.     

Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method.     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.     

依存关系语义角色标注研究

描述了一个基于依存关系的语义角色标注系统,该系统把依存关系作为语义角色标注的基本单元。通过手工或自动标注出来的依存关系,构造出依存关系树,并从树上抽取特征。用最大熵模型对句中谓词的语义角色进行识别和分类。为了消除不必要的结构化信息,在预处理阶段,依存关系树经过了Xue的剪枝算法处理。通过特征工程,丰富的特征及其组合被应用于系统。最终使用 CoNLL 2008 shared task提供的数据作为训练、开发和测试集,使用手工标注的依存关系,F1值达到了86.25%;使用MSTParser自动产生的依存关系,F1值达到了81.66%。     

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.     

Towards portable natural language interfaces based on case-based reasoning

Natural Language Interfaces allow non-technical people to access information stored in Knowledge Bases keeping them unaware of the particular structure of the model or the underlying formal query language. Early research in the field was devoted to improve the performance of a particular system for a given Knowledge Base. Since adapting the system to new domains usually entailed considerable effort, investigating how to bring Portability to NLI became a new challenge. In this article, we investigate how Case-Based Reasoning could serve to assist the expert in porting the system so as to improve its retrieval performance. Our method HITS is based on a novel grammar learning algorithm combined with language acquisition techniques that exploit structural analogies. The learner (system) is able to engage the teacher (expert) with clarification dialogues to validate conjectures (hypotheses and deductions) about the language. Our method presents the following advantages: (i) the customization is naturally defined in the case-based cycle, (ii) the types of questions the system can deal with are not delimited in advance, and (iii) the system ‘reasons’ about precedent cases to deal with unseen questions.     

A connectionist approach for similarity assessment in case-based reasoning systems

Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance for matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function.     

Formulating strategies for stakeholder management: a case-based reasoning approach

The management of competing stakeholders has emerged as an important weapon for strategic management. Typically, reactive, defensive, accommodative, or proactive (RDAP) strategies have been employed for getting into the world of stakeholders. This paper proposes a methodology for formulating strategies for stakeholder management by the use of these RDAP strategies. Our methodology consists of four phases: stakeholder analysis, strategy retrieval, strategy revision, and strategy implementation. Strategies are derived on the basis of similar cases stored in case bases. A system called the stakeholder management strategy support system (SMSS) is implemented to put our methodology to work. In order to demonstrate the practical usefulness of this system, Korean Healthcare IT (Information Technology) industry is illustrated. This illustration implies that our methodology is useful, especially in view of dynamic nature of business and its stakeholders. Our methodology will be able to help any business leader create value while navigating a multi-stakeholder environment.     

Understanding transfer pricing: a case-based reasoning approach

This paper introduces the domain of transfer pricing, followed by an overview of case-based reasoning. Then, case-based reasoning is used to analyse the results of a questionnaire survey in transfer pricing. Finally, some conclusions are drawn and opportunities for future research identified.     

Semantic similarity based approach for reducing Arabic texts dimensionality

An efficient method is introduced to represent large Arabic texts in comparatively smaller size without losing significant information. The proposed method uses the distributional semantics to build the word-context matrix representing the distribution of words across contexts and to transform the text into a vector space model (VSM) representation based on word semantic similarity. The linguistic features of the Arabic language, in addition to the semantic information extracted from different lexical-semantic resources such as Arabic WordNet and named entities’ gazetteers are used to improve the text representation and to create word clusters of similar and related words. Distributional similarity measures have been used to capture the words’ semantic similarity and to create clusters of similar words. The conducted experiments have shown that the proposed method significantly reduces the size of text representation by about 27 % compared with the stem-based VSM and by about 50 % compared with the traditional bag-of-words model. Their results have shown that the amount of dimension reduction depends on the size and shape of the windows of analysis as well as on the content of the text.     

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。     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.     

A fuzzy-rough approach for the maintenance of distributed case-based reasoning systems

Case-based reasoning (CBR) means reasoning from prior examples and it has considerable potential for building intelligent assistant system for the World Wide Web. In order to develop successful Web-based CBR systems, we need to select a set of representative cases for the client side case-base such that this thin client is competence in problem solving. This paper proposes a fuzzy-rough method of selecting cases for such a distributed CBR system, i.e., a thin client system (a smaller case-base with rules) connected to a comparatively more powerful server system (the entire original case-base). The methodology is mainly based on the idea that an original case-base can be transformed into a smaller case-base together with a group of fuzzy adaptation rules, which could be generated using our fuzzy-rough approach. As a result, the smaller case-base with a group of fuzzy rules will almost have the same problem coverage as the entire original case-base. The method proposed in this paper, consists of four steps. First of all, an approach of learning feature weights automatically is used to evaluate the importance of different features in a given case-base. Secondly, clustering of cases is carried out to identify different concepts in the case-base using the acquired feature weights. Thirdly, fuzzy adaptation rules are mined for each concept using a fuzzy-rough method. Finally, a selection strategy which based on the concepts of case coverage and reachability is used to select representative cases. The effectiveness of our method is demonstrated experimentally using some testing data in the travel domain. This project is supported by the Hong Kong Polytechnic University Grant G-V957 and H-ZJ90.     

Introspective reasoning for index refinement in case-based reasoning

Introspective reasoning can enable a reasoner to learn by refining its own reasoning processes. In order to perform this learning, the system must monitor the course of its reasoning to detect learning opportunities and then apply appropriate learning strategies. This article describes lessons learned from research on a computer model of how introspective reasoning can guide failure-driven learning. The computer model monitors its own reasoning by comparing it to a model of the desired behaviour of its reasoning, and learns in response to deviations from the ideal defined by the model. The approach is applied to the problem of determining indices for selecting cases from a case-based planner's memory. Experiments show that learning driven by this introspective reasoning both decreases retrieval effort and improves the quality of plans retrieved, increasing the overall performance of the planning system compared to case learning alone.     

Using a case-based reasoning approach for trading in sports betting markets

The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demonstrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.     

An integrated case-based reasoning approach for intelligent help desk fault management

As modern business functions become more complex and knowledge-intensive, with increasing demands for quality services, there is an emerging trend for organisations to develop and deploy intelligent knowledge-based systems for mission-critical operations. Some of the challenges in successfully implementing this breed of systems depend on how well the intelligent system is integrated with conventional existing information systems and workflow, and the quality of the intelligent system itself. Developing quality expert systems lies in the effective modelling of cognitive processes of human experts and representation of various forms of related knowledge in a domain. An integrated intelligent system called the Intelligent Help Desk Facilitator (IHDF), has been developed for computer and network fault management. The system, which comprises various modules including an expert system, is successfully deployed in a problem response help desk environment of a local bank. This paper describes a cognitive-driven approach to the development of the expert system based on a hybrid knowledge representation and reasoning strategy. The approach incorporates a hybrid case-based reasoning (CBR) framework of techniques which include case memory organisation structures (discrimination networks and shared-featured networks), case indexing and retrieval schemes (fuzzy character-matching, nearest-neighbour similarity matching and knowledge-guided indexing); and an interactive and incremental style of reasoning. The paper discusses the design and implementation of the expert system component of IHDF and illustrates the appropriateness of the hybrid architecture for problem resolution and diagnostic types of applications.     

案例推理分类器属性权重的内省学习调整方法

针对案例推理（CBR）分类器中案例属性权重的分配问题,提出一种基于内省学习的属性权重迭代调整方法。该方法可根据CBR分类器对训练案例分类的结果调整属性的权重。基于成功驱动的权重学习策略,若当前训练案例分类成功,则首先根据权重调整公式增加匹配属性的权重并减少不匹配属性的权重;然后对所有权重进行归一化从而得到当次迭代的新权重。实验结果表明,所提方法的CBR分类器在UCI数据集PD、Heart和WDBC的准确率比传统CBR分类器分别提高1.72%、4.44%和1.05%。故成功驱动的内省学习权重调整方法可以提高权重分配的合理性,进而提高CBR分类器的准确率。