The Conflict Graph for Maintaining Case—Based Reasoning Systems

Iglezakis, Ioannis

doi:10.1007/3-540-44593-5_19

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…It seems beneficial to combine CHN with an adapted local search. In [16] authors prove that swap (1,2) give a good performance. Algorithm (1) show the proposed local search based Swap heuristic.…”

Section: Issn: 2088-8708mentioning

confidence: 97%

“…In [16] authors prove that swap (1,2) give a good performance. Algorithm (1) show the proposed local search based Swap heuristic. As described, this process by replacing each node n in the the given initial solution by two nodes u and v. The chosen u and v must be neighbors of n. Therefore, the solution is improved at each time by adding one node.…”

Section: Issn: 2088-8708mentioning

confidence: 97%

“…All the nodes included in the independent set must respect the condition that they are not pairwise connected by an arc. Max-table is largely applied in many areas: case-based reasoning [1], computer vision [2], scheduling, ... . Max-Stableis a Strong NP-Hard problem while it's hard to be approximate.…”

Section: Introductionmentioning

confidence: 99%

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CHN and Swap Heuristic to Solve the Maximum Independent Set Problem

Bouhouch¹,

Loqman²,

Abderrahime³

2017

IJECE

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We describe a new approach to solve the problem to find the maximum independent set in a given Graph, known also as Max-Stable set problem (MSSP). In this paper, we show how Max-Stable problem can be reformulated into a linear problem under quadratic constraints, and then we resolve the QP result by a hybrid approach based Continuous Hopfeild Neural Network (CHN) and Local Search. In a manner that the solution given by the CHN will be the starting point of the local search. The new approach showed a good performance than the original one which executes a suite of CHN runs, at each execution a new leaner constraint is added into the resolved model. To prove the efficiency of our approach, we present some computational experiments of solving random generated problem and typical MSSP instances of real life problem.

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Section: Issn: 2088-8708mentioning

confidence: 97%

Section: Issn: 2088-8708mentioning

confidence: 97%

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CHN and Swap Heuristic to Solve the Maximum Independent Set Problem

Bouhouch¹,

Loqman²,

Abderrahime³

2017

IJECE

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“…Although every case in the diagnoses process is important, it might be redundant for the system, because maybe the system already has the knowledge to propose that diagnoses, or maybe some similar or more general cases already exists in the system. Maintenance of the system could be implemented using case properties [4,14].…”

Section: ) Matching 4) Adaptation/validation 5) Forgettingmentioning

confidence: 99%

Multiple Sclerosis Diagnoses--Case-Base Reasoning Approach

Kurbalija

Ivanović

Budimac

et al. 2007

Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07)

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Case-Based Reasoning (CBR) is the area of the artificial intelligence in which the new problems are solved by adapting the solutions of the previously successfully solved similar problems. Medicine is a suitable domain for application of CBR because the knowledge of medical experts consists of mixture of textbook knowledge and experience, which consists of cases. Architecture of the system for the diagnoses of multiple sclerosis disease, based on CBR is proposed in this paper.

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“…Within the second set of experiments using the UCI data sets, we conducted more complex tests simulating review and restore for CBM as part of classification tasks (Iglezakis and Anderson 2000). Therefore, we initially split the original data sets into five folds to perform five-fold cross-validation.…”

Section: Experimental Designmentioning

confidence: 99%

Review and Restore for Case‐Base Maintenance

Reinartz

Iglezakis

Roth–Berghofer³

2001

Computational Intelligence

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Case-base maintenance is one of the most important issues for current research in case-based reasoning (CBR). In this article we propose an extended six-step CBR cycle and discuss its two additional steps as part of the maintenance phase of the CBR process. The review step covers assessment and monitoring of the knowledge containers, whereas the restore step actually modifies the contents of the containers according to recommendations resulting from the review step in order to keep the knowledge containers in a usable state. Here we focus our attention on the case base. For the review step, we define several quality measures based on different case and case-base properties that describe specific characteristics of the case base such as correctness, consistency, uniqueness, minimality, and incoherence. Then we use these measures to realize monitoring capabilities for the case-base container that indicate when the restore step is necessary. Finally, we also describe several methods for modifications of the case base in the restore step and their relation to the review step. An initial experimental evaluation shows the appropriateness of the proposed concepts and methods before we conclude the article with a discussion of related work and an outline of future directions to extend these aspects of maintenance in CBR.

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The Conflict Graph for Maintaining Case—Based Reasoning Systems

Cited by 5 publications

References 7 publications

CHN and Swap Heuristic to Solve the Maximum Independent Set Problem

CHN and Swap Heuristic to Solve the Maximum Independent Set Problem

Multiple Sclerosis Diagnoses--Case-Base Reasoning Approach

Review and Restore for Case‐Base Maintenance

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