基于可能性理论的案例决策方法研究

提出基于可能性理论与基于案仍推理相结合的双层CBDT决策方法。第1层：决策者应用基于案例推理方法，通过建立在连续集合上问题的可能性分布，结合不同的决策方法对决策问题的不确定性进行定性分析；第2层：利用期望效用理论在与问题的可能性分布相关联的方案中选择具体方案。该方法可避免以往基于案例决策方法中所蕴含的一些技术难题。     

A CBR-based fuzzy decision tree approach for database classification

Database classification suffers from two well-known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a fuzzy decision tree (FDT), and genetic algorithms (GAs) to construct a decision-making system for data classification in various database applications. The model is major based on the idea that the historic database can be transformed into a smaller case base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller case-based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.     

Database preference queries��a possibilistic logic approach with symbolic priorities

The paper presents a new approach to database preference queries, where preferences are represented in a possibilistic logic manner, using symbolic weights. The symbolic weights may be processed without assessing their precise value, which leaves the freedom for the user to not specify any priority among the preferences. The user may also enforce a (partial) ordering between them, if necessary. The approach can be related to the processing of fuzzy queries whose components are conditionally weighted in terms of importance. In this paper, importance levels are symbolically processed, and refinements of both Pareto ordering and minimum ordering are used. The representational power of the proposed setting is stressed, while the approach is compared with database Best operator-like methods and with the CP-net approach developed in artificial intelligence. The paper also provides a structured and rather broad overview of the different lines of research in the literature dealing with the handling of preferences in database queries.     

基于模糊QoS和偏好权重的Web服务组合方法研究

为了充分体现服务质量(QoS)的不确定性和用户偏好的模糊性,本文将模糊集理论引入基于QoS的Web服务组合中,将不适合精确表示的QoS属性和用户偏好等信息用三角模糊数表示.然后基于权重和法计算模糊总目标,通过设计新的模糊数比较方法,改写Pareto支配关系,将基于模糊数比较的单目标优化问题转化为多目标优化问题,并设计模糊多目标遗传算法(FMOGA)求得Pareto最优解集.该方法不仅能够得到更加贴近实际情况的优化解,同时也解决了多属性决策方法无法对大量候选服务进行全局优化的问题.最后通过实验验证了该算法的有效性和优越性.     

FILIP: A fuzzy intelligent information system with learning capabilities

FILIP (fuzzy intelligent learning information processing) system is designed with the goal to model human information processing. The issues addressed are uncertain knowledge representation and approximate reasoning based on fuzzy set theory, and knowledge acquisition by “being told” or by “learning from examples”. Concepts that can be “learned” by the system can be imprecise (fuzzy), or the knowledge can be incomplete. In the latter case, FILIP uses the concept of similarity to extrapolate the knowledge to cases that were not covered by examples provided by the user. Concepts are stored in the Knowledge Base and employed in intelligent query processing, based on flexible inference that supports approximate matches between the data in the database and the query.

The architecture of FILIP is discussed, the learning algorithm is described, and examples of the system's performance in the knowledge acquisition and querying modes, together with its explanatory capabilities are shown.     

Interactive Case-Based Planning for Forest Fire Management

This paper describes an AI system for planning the first attack on a forest fire. This planning system is based on two major techniques, case-based reasoning, and constraint reasoning, and is part of a decision support system called CHARADE. CHARADE is aimed at supporting the user in the whole process of forest fire management. The novelty of the proposed approach is mainly due to the use of a local similarity metric for case-based reasoning and the integration with a constraint solver in charge of temporal reasoning.     

Application of a hybrid intelligent decision support model in logistics outsourcing

Outsourcing is an increasingly important issue pursued by corporations seeking improved efficiency. Logistics outsourcing or third-party logistics (3PL) involves the use of external companies to perform some or all of the firm's logistics activities. This paper proposes an intelligent decision support framework for effective 3PL evaluation and selection. The proposed framework integrates case-based reasoning, rule-based reasoning and compromise programming techniques in fuzzy environment. This real-time decision-making approach deals with uncertain and imprecise decision situations. Furthermore, the integration of different methodologies takes the advantage of their strengths and complements each other's weaknesses. Consequently, the framework leads to a more accurate, flexible and efficient retrieval of 3PL service providers (alternatives) that are most similar and most useful to the current decision situation. Finally, a real industrial application is given to demonstrate the potential of the proposed framework.     

Knowledge-based treatment planning for adolescent early intervention of mental healthcare: a hybrid case-based reasoning approach

Abstract: Treatment planning is a crucial and complex task in the social services industry. There is an increasing need for knowledge-based systems for supporting caseworkers in the decision-making of treatment planning. This paper presents a hybrid case-based reasoning approach for building a knowledge-based treatment planning system for adolescent early intervention of mental healthcare. The hybrid case-based reasoning approach combines aspects of case-based reasoning, rule-based reasoning and fuzzy theory. The knowledge base of case-based reasoning is a case base of client records consisting of documented experience while that for rule-based reasoning is a set of IF–THEN rules based on the experience of social service professionals. Fuzzy theory is adopted to deal with the uncertain nature of treatment planning. A prototype system has been implemented in a social services company and its performance is evaluated by a group of caseworkers. The results indicate that hybrid case-based reasoning has an enhanced performance and the knowledge-based treatment planning system enables caseworkers to construct more efficient treatment planning in less cost and less time.     

Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules

In this paper, we present a new method, called multiview fuzzy querying, which permits to query incomplete, imprecise and heterogeneously structured data stored in a relational database. This method has been implemented in the MIEL software. MIEL is used to query the Sym'Previus database which gathers information about the behavior of pathogenic germs in food products. In this database, data are incomplete because information about all possible food products and all possible germs is not available; data are heterogeneous because they come from various sources (scientific bibliography, industrial data, etc); data may be imprecise because of the complexity of the underlying biological processes that are involved. To deal with heterogeneity, MIEL queries the database through several views simultaneously. To query incomplete data, MIEL proposes to use a fuzzy set, expressing the query preferences of the user. Fuzzy sets may be defined on a hierarchized domain of values, called an ontology (values of the domain are connected using the a kind of semantic link). MIEL also proposes two optional functionalities to help the user query the database: i) MIEL can use the ontology to enlarge the querying in order to retrieve the nearest data corresponding to the selection criteria; and ii) MIEL proposes fuzzy completion rules to help the user formulate his/her query. To query imprecise data stored in the database with fuzzy selection criteria, MIEL uses fuzzy pattern matching.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

Development of an Intelligent Data-Mining System for a Dispersed Manufacturing Network

Recent advances related to on-line analytical processing (OLAP) have resulted in a significant improvement in data analysis efficiency by virtue of its multidimensional database structure and pre-computing operations of measuring data. However, the research related to the design and implementation of OLAP, particularly in the support of dispersed manufacturing networks in terms of 'intelligent decision making', has yet to be considered as remarkable. Research studies indicate that the level of intelligence of decision support systems can be enhanced with the incorporation of computational intelligence techniques such as case-based reasoning or rule-based reasoning. This paper describes the development of an intelligent data-mining system using a rule-based OLAP approach which can be adopted to support dispersed manufacturing networks in terms of performance enhancement. In this paper, the techniques, methods and infrastructure for the development of such a data-mining system, which possesses certain intelligent features, are presented. To validate the feasibility of this approach, a case example related to the testing of the approach in an emulated industrial environment is covered.     

基于Vague集模糊推理的多评价指标模糊决策方法

目前在智能领域中对Vague集的研究已越来越广泛与深入，并运用于决策问题中，有学者已把Vague集用于多评价指标的模糊决策中，但其决策方法在某些时候却难以得到目标。为此，本文提出了一个基于Vague集模糊推理的多评价指标模糊决策方法。在这个方法中，从基于Vague集的模糊推理的观点来看待模糊决策问题。将评价指标和候选方案之间的关系用一组基于Vague集的推理规则来表示，将决策者的要求用一组Vague集来表示，经过模糊推理等过程最后得到决策结果。然后还给出了一个实例说明这种多评价指标模糊决策方法。这个基于Vague集模糊推理的多评价指标模糊决策方法的提出为决策系统提供了一个有用的工具。     

A decision support system for material and manufacturing process selection

The material and manufacturing process selection problem is a multi-attribute decision-making problem. These decisions are made during the preliminary design stages in an environment characterized by imprecise and uncertain requirements, parameters, and relationships. Material and process selection decisions must occur before design for manufacturing can begin. This paper describes a prototype material and manufacturing process selection system called MAMPS that integrates a formal multi-attribute decision model with a relational database. The decision model enables the representation of the designer's preferences over the decision factors. A compatibility rating between the product profile requirements and the alternatives stored in the database for each decision criteria is generated using possibility theory. The vector of compatibility ratings are aggregated into a single rating of that alternative's compatibility. A ranked set of compatible material and manufacturing process alternatives is output by the system. This approach has advantages over existing systems that either do not have a decision module or are not integrated with a database.     

基于相似Rough集的模糊检索策略

Ｒｏｕｇｈ集理论作为一种具有模糊边界的集合理论，被广泛运用于不确定环境下的信息处理。文章探讨了一种基于相似关系Ｒｏｕｇｈ集的模糊查询技术，它是对普通Ｒｏｕｇｈ集在数据库中应用的推广，能有效地提高查询的灵活度及效率。     

A case-based evolutionary model for defect classification of printed circuit board images

In this research, a case-based evolutionary identification model is developed for PCB defect classification problems. Image understanding is the first and foremost step in the inspection of printed circuit boards. This paper presents a two-phase method for the segmentation of printed circuit board (PCB) images. In the first phase, a set of defect images of several existing basic patterns are stored to form a concept space. In the second phase, a new pattern is evolutionally grabbed using some primitive operators generated by calculating the relative position of several similar cases in the concept space. The case-based reasoning system relies on the software agents derived from past experience within the domain database to determine what feature is required to deliver new patterns in satisfying user’s requirements. Experimental results show that the proposed approach is very effective in identifying the defect patterns.     

Predicting financial activity with evolutionary fuzzy case-based reasoning

Predicting financial activity through examining the short-term liquidity is crucial within today’s turbulent financial environment. Firms, governments, and individuals all need an effective methodology based on liquidity information that plays performance deterioration warning a priori bankruptcy prediction. In this paper, we propose a hybrid decision model using case-based reasoning augmented with genetic algorithms (GAs) and the fuzzy k nearest neighbor (fuzzy k-NN) methods for predicting the financial activity rate. GAs are used to determine the optimal or near-optimal weight vector of financial features expressed in linguistic values by the expert. A fuzzy k-NN-based CBR scheme is designed to compute memberships of financial activity rates and to provide a more flexible and practical mechanism for acquiring, creating, and reusing the expert’s decision knowledge. An empirical experimentation using 746 publicly traded Taiwanese firms shows that the average accuracy of the rating is about 92.36%, which is superior to other related models. The proposed approach not only can lend support to the decision of an expert, but also allow proper feedback for the expert to improve the quality of the decision.