Fuzzy dissimilarity learning in case-based reasoning

1997

An important step in the solution of a target problem in case-based reasoning (CBR) is the retrieval of similar previous cases that can be used to solve the target problem. We review a selection of papers from the CBR literature on aspects of retrieval, such as approaches to the assessment of surface and structural similarity and techniques for automating the construction and maintenance of similarity measures. We also examine a number of retrieval techniques that have been developed to address the limitations of retrieval based purely on similarity.

International Journal of …, 1998

Decision Support Systems, 1997

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 lbr 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. © 1997 Elsevier Science B.V. . by a CBR system is as follows : to assist a decision maker (DM), previous case(s) that closely resemble the new decision problem (new case) is(are) retrie~'ed. The solution of the previous case is then mapped as a solution for the new case. The mapped solution is adapted to account for the differences between a new case and a previous case. For future decision making, feedback of the success or failure of the solution is obtained from the DM.

Information Sciences, 2004

This paper has two main contributions. Firstly, it shows that similarity relations are an adequate means of formalization not only for case retrieval but also for case base building. Secondly, this paper provides a theoretical formalization for building case bases in case-based reasoning (CBR) and presents three algorithms for case base building. The proposed approach argues that case base building can be based on both similarity relations and fuzzy similarity relations, which are both defined on the possible world of problems and solutions respectively. Thus case base building is a form of similarity-based reasoning. This approach is a foundation and an extension for the logical and fuzzy approach to case based reasoning.

2020

Case-Based Reasoning (CBR) and Fuzzy Systems are intended as cognitively more plausible approaches to problem-solving and learning. The two corresponding fields have emphasized different aspects that complement each other in a reasonable way. In the present paper we introduce a fuzzy model for the representation of a CBR system, which is based on the formalization of CBR as a four steps process (retrieve, reuse, revise, retain), and we use the total possibilistic uncertainty as a measurement tool for the effectiveness of the model in solving new related commercial problems. An example is also presented to illustrate our results in practice.

2006

This paper presents a new version of an existing hybrid model for the development of knowledge-based systems, where case-based reasoning is used as a problem solver. Numeric predictive attributes are modeled in terms of fuzzy sets to define neurons in an associative Artificial Neural Network (ANN). After the Fuzzy-ANN is trained, its weights and the membership degrees in the training examples are used to automatically generate a local distance function and an attribute weighting scheme. Using this distance function and following the Nearest Neighbor rule, a new hybrid Connectionist Fuzzy Case-Based Reasoning model is defined. Experimental results show that the model proposed allows to develop knowledge-based systems with a higher accuracy than when using the original model. The model takes the advantages of the approaches used, providing a more natural framework to include expert knowledge by using linguistic terms.

IEEE Transactions on Knowledge and Data …, 2009

Assessing the similarity between cases is a key aspect of the retrieval phase in casebased reasoning (CBR). In most CBR work, similarity is assessed based on feature-value descriptions of cases using similarity metrics which use these feature values. In fact it might be said that this notion of a feature-value representation is a defining part of the CBR world-view -it underpins the idea of a problem space with cases located relative to each other in this space. Recently a variety of similarity mechanisms have emerged that are not founded on this feature-space idea. Some of these new similarity mechanisms have emerged in CBR research and some have arisen in other areas of data analysis. In fact research on Support Vector Machines (SVM) is a rich source of novel similarity representations because of the emphasis on encoding domain knowledge in the kernel function of the SVM. In this paper we present a taxonomy that organises these new similarity mechanisms and more established similarity mechanisms in a coherent framework.

The article discusses some applications of fuzzy logic ideas to formalizing of the Case-Based Reasoning (CBR) process and to measuring the effectiveness of CBR systems

2017

Case-based reasoning (CBR) is a problem solving technique that uses previous experiences to solve new problems. Among the four phases of CBR, Retrieval is the first and the most important phase, as it lays the foundation of the entire CBR cycle. Retrieval aims to retrieve similar cases from the case-base, given a new situation. CBR systems typically use a strategy called similarity-based retrieval for retrieving cases. One of the derivatives of similarity-based retrieval is k-nearest neighbor (k-NN) algorithm. In this paper, we compare the performances of k-NN, Fuzzy nearest neighbor (Fuzzy NN) and Genetic Programming (GP) classifiers for retrieval of cases. We evaluate these algorithms in WEKA, with benchmark data sets for classification from UCI.

1997

In the field of case-based reasoning in artificial intelligence, the general derivation of so-called similarity measures is still an unresolved open question. In this work the theoretical framework of dimensional analysis is used to derive appropriate similarity measures for a case-based reasoning technique. For the subclass of all case descriptions in engineering and physics consisting of real-valued quantities with physical units, it is shown how the Pi-Theorem of Buckingham can be used to construct similarity measures from these case descriptions. The necessary functional model assumptions are defined and the theoretical foundations are discussed. Within this functional model approach a proof for the correctness of the case-based and rule-based reasoning technique can be derived. An application of the case-based reasoning technique based on dimensionless groups is demonstrated using the reasoning on power efficiency in gas turbines and is compared to a conventional approach using a Voronoi-technique.