Classification in the Retrieval Phase of Case-based Reasoning

Expert Systems with Applications, 1995

Case-based reasoning is a technique recently developed to alleviate limitations of the rulebased expert systems. Instead of relying solely on rules, a case-based system maintains old cases in a case base. When a new problem is encountered, the system retrieves similar cases from the case base and constructs a solution to the new problem based on existing solutions. A key issue in casebased reasoning is how to index and retrieve similar cases. In this paper, we present a new approach that integrates fuzzy set concepts into the case indexing and retrieval process. This approach has a few advantages over existing methods. First, it allows numerical features to be converted into fuzzy terms to simplify the matching process. Second, it allows cases in different domains to be comparable. Finally, it allows greater flexibility in the retrieval of candidate cases.

2016

Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and CaseBased Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features. Keywords—Case based reasoning, classification, expert's knowledge, hybrid model.

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.

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1997

Case-based reasoning (CBR) supports ill-structured decision making by retrieving previous cases that are useful toward the solution of a new decision problem. The usefulness of previous cases is determined by assessing the similarity of a new case with the previous cases. In this paper, we present a modified form of the cosine matching function that makes it possible to contrast the two cases being matched and to include differences in the importance of features in the new case and the importance of features in the previous case. Our empirical evaluation of a CBR application to a diagnosis and repair task in an electromechanical domain shows that the proposed modified cosine matching function has a superior retrieval performance when compared to the performance of nearest-neighbor and the Tversky's contrast matching functions. Recent CBR systems mainly retrieve previous cases to offer decision support [1], . The retrieval of relevant previous cases is critical to their success . Central to retrieval methodologies is the assessment of the similarity of a new others.

Lecture Notes in Computer Science, 2013

Case retrieval is one important step in the case-based reasoning cycle. Up to now, several algorithms have been proposed for the indexing of cases, since the original indexing approach of k-d trees came up in literature. Main approaches propose the use a precomputed binary search tree to get an average logarithmic time effort in searching. The proposal presented in this paper consists of an indexing algorithm based on the principle of binary search trees for efficient case retrieval according to a given similarity measure called sim. The proposed NIAR k-d tree algorithm embodies two main steps based on the computation of the average value of the corresponding attribute among the subtree cases, and selecting for that attribute, the value of the Nearest Instance/case to the Average as the Root (partition value). Experimental results with some databases have shown that the retrieval step in NIAR k-d tree is faster than the standard k-d tree approach. The time efficiency, the depth and breadth in both trees are analyzed. The results obtained depict a significant difference of levels in the trees. The presented approach is implemented within a current research work on introspective reasoning framework for case-based reasoning in continuous domains.

10.1109/INFOCOMAN.2016.7784214, 2016

The aim of this paper was to perform an extension of (CBRAR) strategy, Case-Based Reasoning using Association Rules to enhance the performance of the Similarity Base Retrieval SBR. FP-CAR classed frequent pattern tree algorithms are used in order to select correctly retrieved cases in Case-Based Reasoning (CBR). Class Association Rules (CARs) are utilized to generate an optimum FP-tree which holds a value of each node. The potential benefit offered is that more efficient results can be obtained when the retrieval phase returns uncertain answers. A comparison of CBR Query with FP-CAR is performed as patterns in order to identify the longest length nodes of the voted class. If the patterns are matched, the proposed CBRAR can choose the most similar and correct case. The experimental evaluation on a real dataset shows that the proposed CBRAR is a superior approach when likened to the accuracy of the CBR systems used in this investigation.

Case-Based Reasoning (CBR) is one of most successful applied AI technologies of recent years. Commercial and industrial applications can be developed rapidly and existing corporate databases can be used as knowledge resources. Helpdesks and diagnostic systems are the most common applications. CBR techniques can be applied to large and complex datasets. In this paper we are aiming to find each unique feature for each case in a Case-Base using a technique to build Power Set Tree (PST) using adding feature, Each possible reduct is added to a node in the tree, After building the PST we will perform a Breadth-First search to prune this tree and reduce the number of possibilities by deleting each unique feature node and its children, that would help in getting the most accurate unique features with minimum number of comparisons.

Neural Computing & Applications, 2001

In this survey paper, the-state-of-art of the connectionist model (i.e. Artificial Neural Network (ANN)) based methodology for a Case-Based Reasoning (CBR) system design is discussed. Special emphasis is laid on how the ANN can advance CBR technology by building an ANN-based CBR system, or integrating itself as a component within a CBR system. Several ANN models proposed for constructing a CBR system and for solving some special issues involved in a CBR process are described. The main characteristics of each model are analysed, and the advantages and limitations of different models are compared. Also, future research directions are outlined.

Proceedings of …, 1997

This paper is an attempt at providing a fuzzy set-based formalization of case-based reasoning. The proposed approach, which does not take into account the learning aspects of case-based reasoning, assumes a principle stating that "the more similar are the problem description attributes, the more similar are the outcome attributes". A weaker form of this principle concluding only on the graded possibility of the similarity of the outcome attributes, is also considered. These two forms of the case-based reasoning principle are modelled in terms of fuzzy rules. Then an approximate reasoning machinery taking advantage of this principle enables us to apply the information stored in the memory of previous cases to the current problem. 1-INTRODUCTION Case-based reasoning amounts to inferring from known case(s) which are similar enough to a newly encountered situation, that what is true in the known case(s) might still be true, up to some suitable transposition, in this new situation. Case-based reasoning may be viewed as a particular form of analogical reasoning, since an analogy is a relational statement of the form, D is in the same relation w.r.t. C as B is w.r.t. A, which is usually seen as a way of "guessing" D, knowing A, B and C. Analogical reasoning has been investigated for a long time in Artificial Intelligence and the interest in this research has been considerably renewed by the development of case-based reasoning (Kolodner, 1993). Any attempt to provide a general formalization of this common way of reasoning raises several critical issues, especially on such questions as i) the definition and practical elicitation of similarity measures; ii) the retrieving of relevant cases; iii) the logical modelling of the inference mechanism; iv) the adaptation of the retrieved cases in order to extrapolate plausible values for the variable(s) of interest in the current situation; v) the incomplete, imprecise, or even uncertain, description of available cases, or of the current problem.

2003

This work is the second part of a general similarity-based theory of case-based reasoning (CBR), which moves CBR towards a firm theoretical foundation based on similarity-based reasoning. This paper will first examine abductive CBR and deductive CBR and propose a knowledge-based model for integrating abductive CBR and deductive CBR. It then proposes similarity-based models for rule-based and fuzzy rule-based case retrieval. Finally it investigates similarity-based case adaptation.