Hierarchical fuzzy case based reasoning with multi-criteria decision making for financial applications
18 Dec 2007-pp 226-234
TL;DR: This paper presents a framework for using a case-based reasoning system for stock analysis in financial market using a hierarchical structure for case representation and incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario.
Abstract: This paper presents a framework for using a case-based reasoning system for stock analysis in financial market. The unique aspect of this paper is the use of a hierarchical structure for case representation. The system further incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario. Two important aspects of financial market are addressed in this paper: stock evaluation and investment planning. CBR and multi-criteria when used in conjunction offer an effective tool for evaluating goodness of a particular stock based on certain factors. The system also suggests a suitable investment plan based on the current assets of a particular investor. Stock evaluation maps to a flat case structure, but investment planning offers a scenario more suited for structuring the case into successive detailed layers of information related to different facets. This naturally leads to a hierarchical case structure.
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TL;DR: This paper proposes a fuzzy ontology-based CBR framework that combines a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types and achieves an accuracy of 97.67%.
98 citations
TL;DR: An evolutionary algorithm based approach for selection of topologies in hierarchical fuzzy systems (HFS) is presented and Coupling fuzzy system with evolutionary algorithm provides a solution to the automated acquisition of the fuzzy rule base.
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.
19 citations
TL;DR: 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 is presented.
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.
13 citations
Cites methods from "Hierarchical fuzzy case based reaso..."
...This approach is applied by Tsatsoulis, Cheng and Wei (1997) for designing pharmaceutical and by Sushmita and Chaudhury (2007) for selection of investment road map in stock market....
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...…influence diagram and applied to a project proposal review process (Lee and Kim, 2002), as CaBMA system for capturing, refining, reusing and indicating possible repairs for project plans (Xu and Muñoz-Avila, 2008) and for stock analysis in financial market (Sushmita and Chaudhury, 2007)....
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01 Jul 2019
TL;DR: A case-base preparation framework for CBR systems, which converts the electronic health record medical data into fuzzy CBR knowledge, which enhances the representation of case- base knowledge, the performance of retrieval algorithms, and the querying capabilities ofCBR systems.
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.
11 citations
TL;DR: The proposed framework, integrating fuzzy linguistic GDM and CBR, thus enhances the efficiency and effectiveness of a CBR system and provides a powerful methodology for performance ranking.
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
9 citations
Cites result from "Hierarchical fuzzy case based reaso..."
...In contrast to most studies utilizing CBR to aid decision-making [2,6,8,73], the proposed framework takes advantage of MAGDM to build up high-quality cases and thus promote the quality and capability of CBR....
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References
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TL;DR: The main features of real-world problems for which the outranking approach is appropriate and the concept of outranking relations are described and the definition of such out ranking relations is given for the main ELECTRE methods.
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.
1,751 citations
"Hierarchical fuzzy case based reaso..." refers background in this paper
...ELECTRE [9], [10] and PRMETHEE [11] are some of the ranking function that could be used to outrank one similar case over the other based on certain parameters....
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TL;DR: An overview of the current research and development directions in knowledge and data engineering is provided, with respect to programmability and representation, design tradeoffs, algorithms and control, and emerging technologies.
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. >
708 citations
TL;DR: The PROMETHEE Methods are particularly appropriate to treat multicriteria problems of the following type:==================¯¯¯¯¯¯¯¯¯¯676======676============672======676676======672¯¯676¯¯672======671======676¯¯671======672¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯677======676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯671¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯672¯¯671¯¯676¯¯¯¯¯¯¯¯¯¯672¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯676』676======671¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯¯¯¯¯672』672======672』676¯¯682======676』672¯¯672¯¯¯¯¯¯¯¯¯¯671』676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
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.
93 citations
TL;DR: The technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction, is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design.
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.
82 citations
"Hierarchical fuzzy case based reaso..." refers background in this paper
...Although, work like [5] and [6] presented the concept of a hierarchical CBR, but the concept proposed involve reusing of multiple cases at various levels of abstraction....
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TL;DR: A new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR), which originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area is proposed.
Abstract: This paper proposes a new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR). The main idea of DAE CBR originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index.
67 citations
"Hierarchical fuzzy case based reaso..." refers background in this paper
...[2] proposed the daily financial condition indicator (DFCI) monitoring financial market built on CBR, [3], in their work proposed a new learning A....
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...Although, work like [5] and [6] presented the concept of a hierarchical CBR, but the concept proposed involve reusing of multiple cases at various levels of abstraction....
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...: PReMI 2007, LNCS 4815, pp. 226–234, 2007. c© Springer-Verlag Berlin Heidelberg 2007 technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR, which again deals with the prediction of the overall stock market....
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...[2] proposed the daily financial condition indicator (DFCI) monitoring financial market built on CBR, [3], in their work proposed a new learning A. Ghosh, R.K. De, and S.K. Pal (Eds.)...
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