Maintaining case knowledge vocabulary using a new evidential attribute clustering method

Ayed, Safa Ben; Elouedi, Zied; Lefèvre, Éric

doi:10.1142/9789813273238_0046

Cited by 4 publications

(14 citation statements)

References 14 publications

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“…Actually, the maintenance of features is widely explored within the context of Feature Selection (FS) 7 or Feature Reduction (FR). Hence, we find, in the literature, several works that select, reduce or delete features describing cases to ensure accurate retrieval outcomes, such that in [10,21,22,23]. One among the useful concepts to select relevant features within CBR is the Attribute Clustering which has been carried out, for that matter, in several works [10,24,25].…”

Section: Vocabulary Maintenancementioning

confidence: 99%

“…Hence, we find, in the literature, several works that select, reduce or delete features describing cases to ensure accurate retrieval outcomes, such that in [10,21,22,23]. One among the useful concepts to select relevant features within CBR is the Attribute Clustering which has been carried out, for that matter, in several works [10,24,25]. Similarly to object clustering, features belong to the same cluster are somehow similar.…”

Section: Vocabulary Maintenancementioning

confidence: 99%

“…While maintaining vocabulary for CBR systems, it is important to take into account and preserve the relation between cases features. That's why, recommendations are offered [10,24] to regroup features during the learning step in order to eliminate redundant and irrelevant attributes. Besides, attribute clustering provides a flexibility to CBR systems by offering the possibility to replace any feature by another one belonging to the same cluster.…”

Section: Vocabulary Maintenancementioning

confidence: 99%

“…• The first phase, that is inspired from our preliminary work described in [10], concerns vocabulary maintenance.…”

Section: Integration Of Cbm and Vocabulary Maintenance Policies Within The Belief Function Frameworkmentioning

confidence: 99%

“…However, only some of them are relevant for the decision making about a particular task. For that reason, the vocabulary container presents also the subject of a considerable maintenance target [10] to provide a competent and up-to-date CBR system.…”

Section: Introductionmentioning

confidence: 99%

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An evidential integrated method for maintaining case base and vocabulary containers within CBR systems

Ayed

Elouedi

Lefèvre

2020

Information Sciences

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Cases and vocabulary maintenance presents a crucial task to preserve high competent Case-Based Reasoning (CBR) systems, since the accuracy of their offered solutions are strongly dependent on stored cases and their describing attributes quality. The maintenance aims generally at eliminating two types of undesirable knowledge which are noisy and redundant data. However, inexpedient Case Base Maintenance (CBM) or vocabulary maintenance may not only greatly decrease CBR competence in solving new problems, but also reduce its performance in term of retrieval time. Besides, to provide a high maintenance quality, it is necessary to manage uncertainty within knowledge since "real-world data are never perfect" and stored cases within a CBR system's Case Base (CB) describe realworld experiences. Hence, we propose, in this paper, a new integrated method that maintains both of the CB and the vocabulary knowledge containers of CBR systems by offering a new alternating technique to properly detect noisiness and redundancy whether in cases or features. During the learning steps of our new integrated maintenance policy, which drives the decision making about cases and attributes selection, we manage uncertainty using one among the most powerful tools called the Belief Function Theory.

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Section: Vocabulary Maintenancementioning

confidence: 99%

Section: Vocabulary Maintenancementioning

confidence: 99%

Section: Vocabulary Maintenancementioning

confidence: 99%

“…• The first phase, that is inspired from our preliminary work described in [10], concerns vocabulary maintenance.…”

Section: Integration Of Cbm and Vocabulary Maintenance Policies Within The Belief Function Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An evidential integrated method for maintaining case base and vocabulary containers within CBR systems

Ayed

Elouedi

Lefèvre

2020

Information Sciences

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CEVM: Constrained Evidential Vocabulary Maintenance Policy for CBR Systems

Ayed¹,

Elouedi²,

Lefèvre³

2019

Lecture Notes in Computer Science

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The maintenance of Case-Based Reasoning (CBR) systems has attracted increasing interest within current research since they proved high-quality results in different real-world domains. This kind of systems stores previous experiences, which are described by a vocabulary (e.g., attributes), incrementally in a case base. Actually, the vocabulary presents one among the most important maintenance targets, since it highly contributes in providing accurate solutions and in improving systems' performance, especially within high-dimensional domains. However, there is no policy, in the literature, that offers the ability to exploit prior knowledge (e.g., given by domain-experts) during the maintenance of features describing cases. In this paper, we propose a flexible policy for the most relevant attribute selection based on the attribute clustering concept. This new policy is able, on the one hand, to manage uncertainty using the belief function theory based tools, and on the other hand, to make use of domain-experts knowledge in form of pairwise constraints: If two attributes offer the same information without any added-value, then a Must-link constraint between them is generated. Otherwise, if there is no relation between them and they offer different information, then a Cannot-link constraint between them is created.

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CIMMEP: constrained integrated method for CBR maintenance based on evidential policies

2021

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The quality of the proposed solutions by Case-Based Reasoning (CBR) systems is highly dependent on recorded experiences and their describing attributes. Hence, to keep them offering accurate and efficient responses for a long time frame, the maintenance of Case Bases (CB) and Vocabulary knowledge is required. However, maintenance operations are usually unable to exploit provided domain-experts knowledge although this kind of systems are widely applied in several real-life contexts. This offered prior knowledge is handled, in our work, in form of pairwise constraints: Regarding cases, Must-Link (ML) affirms that two given problems should have the same solution, and Cannot-Link (CL) informs that two problems cannot have the same solution. These constraints may also regard vocabulary knowledge in such a way that ML is generated when prior knowledge affirm that two given features offer correlated values, therefore, similar information, and CL is built when they provide different information. This paper proposes a new constrained & integrated method, named CIMMEP, encoding Constrained & Integrated Maintaining Method based on Evidential Policies, for maintaining both vocabulary and CB through eliminating redundancy and noisiness. Since CBR systems handle real-world experiences, which are full of uncertainty, CIMMEP manages this imperfection using a powerful tool called the belief function theory.

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Maintaining case knowledge vocabulary using a new evidential attribute clustering method

Cited by 4 publications

References 14 publications

An evidential integrated method for maintaining case base and vocabulary containers within CBR systems

An evidential integrated method for maintaining case base and vocabulary containers within CBR systems

CEVM: Constrained Evidential Vocabulary Maintenance Policy for CBR Systems

CIMMEP: constrained integrated method for CBR maintenance based on evidential policies

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