Hierarchical fuzzy case based reasoning with multi-criteria decision making for financial applications

Abstract: Propose a fuzzy ontology based semantic-CBR framework.Propose a novel OWL2 fuzzy case-base ontology.Propose a fuzzy semantic case retrieval algorithm using an SNOMED CT fragment.Implement the fuzzy KI-CBR system using diabetes diagnosis as a case study.Combine fuzzy logic and ontology semantics in CBR enhances the CBR accuracy. ObjectiveCase-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. MethodsKnowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. MaterialThis framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. ResultsThe resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. ConclusionBuilding an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems.

Abstract: An evolutionary algorithm based approach for selection of topologies in hierarchical fuzzy systems (HFS) is presented. Coupling fuzzy system with evolutionary algorithm provides a solution to the automated acquisition of the fuzzy rule base. It is difficult to study the problem of hierarchical decomposition for a large class of fuzzy systems but it is possible to analyse such architectures on the example of a particular fuzzy system, such as inverted pendulum. Topology of the HFS must be selected according to the physical properties of the dynamical system under consideration. Different HFS topologies for an inverted pendulum system are investigated and analysed to address the problem of how input configuration in multi-layered structure affects the controller performance. The experiments are conducted to test controller performance for different topologies of the hierarchical fuzzy system. The impact of different topologies on control process is discussed. The results from the case study of inverted pendulum can be extended to other dynamical systems.

Abstract: This paper presents a model to support the banking managerial decisions in the evaluation of investment plans, especially on rejecting inappropriate plans that can be done in short time (less than hour) and with minimal cost. Because there are some uncertainties in the evaluation process, our proposed model utilises fuzzy set theory to define the problem space in which an acceptance or rejection decision for a submitted investment plan is made. The model is based on lessons-learned concept and developed through the combination of case-based reasoning (CBR) and multiple attribute decision making in fuzzy environment. The model uses an enhanced version of CBR in which a novel concept as solution's truth value is implemented. A set of investment plans is evaluated to show the applicability and efficiency of the model. Different scenarios in terms of sensitivity analysis are also mentioned to capture managerial insights. Comparing the obtained results of the model with those of other algorithms shows its better proximity to human reasoning and decision making.

Abstract: Medical case-based reasoning (CBR) systems require the handling of vague or imprecise data. The fuzzy set theory is particularly suitable for this purpose. This paper proposes a case-base preparation framework for CBR systems, which converts the electronic health record medical data into fuzzy CBR knowledge. It generates fuzzy case-base knowledge by suggesting a standard crisp entity–relationship data model for CBR case-base. The resulting data model is fuzzified using a proposed relational data model fuzzification methodology. The performances of this methodology and its resulting fuzzy case-base structure are evaluated. Diabetes diagnosis is used as a case study. A set of 60 real diabetic cases is used in the study. A fuzzy CBR system is implemented to check the diagnoses accuracy. It combines the resulting fuzzy case-base with a proposed fuzzy similarity measure. Experimental results indicate that the proposed fuzzy CBR method is superior to traditional CBR and other machine-learning methods. Our fuzzy CBR achieves an accuracy of 95%, a precision of 96%, a recall 97.96%, an f-measure of 96.97%, a specificity of 81.82%, and good robustness for dealing with vagueness. The resulting fuzzy case-base relational database enhances the representation of case-base knowledge, the performance of retrieval algorithms, and the querying capabilities of CBR systems.

Abstract: Organizing a reliable case base, which serves as a repository of experience, is crucial for the success of a case-based reasoning (CBR) system To ensure that such repositories contain high-quality cases, this paper proposes a framework employing the methodology of fuzzy linguistic group decision-making (GDM) in the context of multiple attributes The overall process of MAGDM could be analogous to the memory-related behaviors of the human brain, in which knowledge is elicited and validated, as in the short-term memory, and then eventually integrated into the long-term memory to serve as solutions to build-up the number of high-quality cases Moreover, the proposed approach is flexible, as it enables experts to define the set of the parameters of the membership functions associated with labels, thus improving the quality of the linguistic term sets and leading to better assessments Furthermore, the proposed KC index, characterized by measures of both individual and group consistencies, can provide a more effective assessment to assign suitable experts' weights than most existing GDM models This is supported by the experimental results presented in this work, indicating that the KC index can indeed lead to a more satisfactory overall level of consensus In addition, the mutual validation between the set of the parameters of the membership functions associated with labels by experts and the evaluation of the experts' weights can be manifested in terms of the KC indexThe extended collective decision matrix derived from the process of MAGDM that is used to construct case bases is more practical and effective than other approaches, as its elements are meaningful and interpretable The proposed framework, integrating fuzzy linguistic GDM and CBR, thus enhances the efficiency and effectiveness of a CBR system This is further evidenced in the results of an experiment, which show that this hybrid framework is very effective in implementing a case-based knowledge system and provides a powerful methodology for performance ranking

Abstract: In the first part of this paper, we describe the main features of real-world problems for which the outranking approach is appropriate and we present the concept of outranking relations. The second part is devoted to basic ideas and concepts used for building outranking relations. The definition of such outranking relations is given for the main ELECTRE methods in Part 3. The final part of the paper is devoted to some practical considerations.

Abstract: The authors provide an overview of the current research and development directions in knowledge and data engineering. They classify research problems and approaches in this area and discuss future trends. Research on knowledge and data engineering is examined with respect to programmability and representation, design tradeoffs, algorithms and control, and emerging technologies. Future challenges are considered with respect to software and hardware architecture and system design. The paper serves as an introduction to this first issue of a new quarter. >

Abstract: The PROMETHEE Methods are particularly appropriate to treat multicriteria problems of the following type: $$Max\,\left\{ {{f_1}(x),{f_2}(x),...,{f_j}(x),...,{f_k}(x)|x \in A} \right\}$$ (1.1) for which A is a finite set of possible alternatives and fj(x), j = 1, 2,…,k a set of k evaluation criteria.

Abstract: Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design.

Abstract: This paper presents a simultaneous optimization method of a case-based reasoning (CBR) system using a genetic algorithm (GA) for financial forecasting. Prior research proposed many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most research used the GA for improving only a part of architectural factors of the CBR model. However, the performance of the CBR model may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of the CBR model outperforms other conventional approaches for financial forecasting.