Evaluating Case-Base Maintenance algorithms

Lupiani, Eduardo; Juárez, José M.; Palma, José

doi:10.1016/j.knosys.2014.05.014

Cited by 16 publications

(10 citation statements)

References 44 publications

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“…Ferrandiz and Boullé (2010) on the other hand, used ten-fold cross validation. This might bias the results as mentioned in Lupiani's recent work (Lupiani et al, 2014). But this gives us an estimation of methods' performances with respect to each other, without having to develop them.…”

Section: General Studymentioning

confidence: 92%

“…Some authors have proposed different taxonomies of CBM algorithms (Lupiani et al, 2014;Garcia et al, 2012). Garcia et al (2012) describe the different properties issued from variable selection and adapted to the PS methods, the properties specific to the prototyping selection methods and at the end the properties that can influence the results of an instance selection algorithm in combination with a given classifier.…”

Section: Prototyping Selection Methodsmentioning

confidence: 99%

“…Searching for efficient approaches in instance reduction is still an active field of research (Hamidzadeh et al, 2014, Garcia et al, 2012, Lupiani et al 2014, Leyva et al, 2015. A most common technique to reduce the size of the training set, to decrease computational cost and sensitiveness to noise is the use of Prototype Selection method PS (Calvo-Zaragoza et al, 2014).…”

Section: Optimization Policies or Prototyping Selectionmentioning

confidence: 99%

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Case-based maintenance: Structuring and incrementing the case base

Chebel-Morello

Haouchine

Zerhouni

2015

Knowledge-Based Systems

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Please cite this article as: B. Chebel-Morello, M.K. Haouchine, N. Zerhouni, Case-based Maintenance: Structuring and incrementing the Case Base, Knowledge-Based Systems (2015), doi: http://dx. Abstract.To avoid performance degradation and maintain the quality of results obtained by the case-based reasoning (CBR) systems, maintenance becomes necessary, especially for those systems designed to operate over long periods and which must handle large numbers of cases. CBR systems cannot be preserved without scanning the case base. For this reason, the latter must undergo maintenance operations. The techniques of case base's dimension optimization is the analog of instance reduction size methodology (in the machine learning community). This study links these techniques by presenting case-based maintenance in the framework of instance based reduction, and provides: first an overview of CBM studies, second, a novel method of structuring and updating the case base and finally an application of industrial case is presented.The structuring combines a categorization algorithm with a measure of competence CM based on competence and performance criteria. Since the case base must progress over time through the addition of new cases, an auto-increment algorithm is installed in order to dynamically ensure the structuring and the quality of a case base. The proposed method was evaluated through a case base from an industrial plant. In addition, an experimental study of the competence and the performance was undertaken on reference benchmarks. This study showed that the proposed method gives better results than the best methods currently found in the literature.

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Section: General Studymentioning

confidence: 92%

Section: Prototyping Selection Methodsmentioning

confidence: 99%

Section: Optimization Policies or Prototyping Selectionmentioning

confidence: 99%

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Case-based maintenance: Structuring and incrementing the case base

Chebel-Morello

Haouchine

Zerhouni

2015

Knowledge-Based Systems

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“…Despite the great success of CBR in solving problems related to knowledge reuse, there are shortcomings with regard to its learning capability, as follows: (i) it suffers from difficulties in managing new case structures with different structures of past cases [51]; (ii) system performance would degrade due to the addition of new cases to the case base, since more cases would mean that more time is needed to retrieve cases similar to an input query [45]; and (iii) more storage is required as more new cases are added [3].…”

Section: Case-based Reasoningmentioning

confidence: 99%

“…This has raised the need for case base maintenance (CBM), the aim of which is to ensure an acceptable query execution time for the CBR system, and to facilitate future reasoning for a particular set of performance objectives [38]. There are two strategies that can be used to achieve CBM [45]. First, optimizing the software that implements the case-base, e.g., tuning the parameters of the retrieval step or changing the case structure and removing redundant or noisy cases from the case-bases.…”

Section: Case-based Reasoningmentioning

confidence: 99%

Using mutually validated memories of experts for case-based knowledge systems

2015

Knowledge-Based Systems

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Toward the Evaluation of Case Base Maintenance Policies Under the Belief Function Theory

Ayed¹,

Elouedi²,

Lefèvre³

2019

Lecture Notes in Computer Science

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The life cycle of Case-Based Reasoning (CBR) systems implies the maintenance of their knowledge containers for reasons of efficiency and competence. However, two main issues occur. First, knowledge within such systems is full of uncertainty and imprecision since they involve real-world experiences. Second, it is not obvious to choose from the wealth of maintenance policies, available in the literature, the most adequate one to preserve the competence towards problems' solving. In fact, this competence is so difficult to be actually estimated due to the diversity of influencing factors within CBR systems. For that reasons, we propose, in this work, an entire evaluating process that allows to assess Case Base Maintenance (CBM) policies using information coming from both a statistical measure and a competence model under the belief function theory.

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Evaluating Case-Base Maintenance algorithms

Cited by 16 publications

References 44 publications

Case-based maintenance: Structuring and incrementing the case base

Case-based maintenance: Structuring and incrementing the case base

Using mutually validated memories of experts for case-based knowledge systems

Toward the Evaluation of Case Base Maintenance Policies Under the Belief Function Theory

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