A Case-Based Reasoning Approach to Formulating University Timetables Using Genetic Algorithms

Grech, Alicia; Main, Julie

doi:10.1007/11552413_12

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…They were fixed thanks to expert knowledge [17], making them interpretable and not database dependent. Learningbased approaches, such as GAs [17,51] were also used to weight the attributes. However, some CBR systems assigned the same importance to each attribute [38].…”

Section: Related Workmentioning

confidence: 99%

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Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

et al. 2020

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In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps-retrieve, reuse, revise, and retain. The proposed casebased reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve-and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-ofthe-art similarity measures.

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Section: Related Workmentioning

confidence: 99%

“…Using a GA to learn attribute weights; no prior expert knowledge is integrated in this last approach. GAs have already been adopted successfully in several CBR systems for weight determination [20,51]. In this approach, a floating-point chromosome representation is used to represent an individual.…”

Section: Evaluation Approachmentioning

confidence: 99%

Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

et al. 2020

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“…It is also worth noting that some researchers employed evolutionary algorithms for solution revision. These research works have been found in [223][224][225].…”

Section: Review Of Solution Reuse and Revisionmentioning

confidence: 99%

Contributions to Case-Based Reasoning Enabled Decision Support System for Smart Agriculture

Zhai¹

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An Evolutionary Approach to Intelligent Planning

Mehta

Sachdeva²,

Bhargava

et al. 2010

Swarm, Evolutionary, and Memetic Computing

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A Case-Based Reasoning Approach to Formulating University Timetables Using Genetic Algorithms

Cited by 5 publications

References 8 publications

Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

Contributions to Case-Based Reasoning Enabled Decision Support System for Smart Agriculture

An Evolutionary Approach to Intelligent Planning

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