Genetic Algorithms to Optimise CBR Retrieval

Jarmulak, Jacek; Craw, Susan; Crowe, Raymond

doi:10.1007/3-540-44527-7_13

Cited by 39 publications

(27 citation statements)

References 11 publications

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“…The likelihood of seeing stronger similarities is much higher if the number of cases is substantially higher than 29. Also, the study concentrated on optimizing attribute weights, while it should also be possible to improve attribute selection by using GA ͑Jarmulak and Craw 1999; Jarmulak et al 2000͒, vary the number and combination of attributes, increase the number of cases considered, adjust the number and size of training and test sets, and try nondefault values for the parameters used in the various weight generation approaches. Considering the large number of alternative combinations of these variations, the study presented in this paper is limited to the basics and the experimentation with these variations are left for future research.…”

Section: Discussionmentioning

confidence: 99%

“…Constructing CBR systems requires a significant knowledge engineering effort ͑Cunningham and Bonzana 1999͒. The knowledge acquisition effort can be minimized by determining the most representative case attributes, optimizing the case base organization and case retrieval, and refining the process of similarity assessment ͑Jar-mulak and Craw 1999; Jarmulak et al 2000͒. Similarity assessment involves a systematic comparison of the attributes of a test case with the attributes of all cases in the case base.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems

Doğan

Arditi

Günaydın

2006

J. Constr. Eng. Manage.

103

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This paper compares the performance of three optimization techniques, namely feature counting, gradient descent, and genetic algorithms ͑GA͒ in generating attribute weights that were used in a spreadsheet-based case based reasoning ͑CBR͒ prediction model. The generation of the attribute weights by using the three optimization techniques and the development of the procedure used in the CBR model are described in this paper in detail. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of 29 residential building projects. The results indicated that GA-augmented CBR performed better than CBR used in association with the other two optimization techniques. The study is of benefit primarily to researchers as it compares the impact attribute weights generated by three different optimization techniques on the performance of a CBR prediction tool.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems

Doğan

Arditi

Günaydın

2006

J. Constr. Eng. Manage.

103

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“…The application of GA to case-based reasoning has been studied to the retrieval phase for feature and instance learning, as in [54,55,56]. We understand that in the future we need to complement our approach with these studies and consider the synergies between feature learning and classifier weight learning, together with local similarity measures, such as those studied in [57].…”

Section: Comparison With Similar Toolsmentioning

confidence: 99%

eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis

Plà

López

Gay

et al. 2013

Decision Support Systems

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In this work we propose a user-friendly medically oriented tool for prognosis development systems and experimentation under a case-based reasoning methodology. The tool enables health care collaboration practice to be mapped in cases where different doctors share their expertise, for example, or where medical committee composed of specialists from different fields work together to achieve a final prognosis. Each agent with a different piece of knowledge classifies the given cases through metrics designed for this purpose. Since multiple solutions for the same case is useless, agents collaborate among themselves in order to achieve a final decision through a coordinated schema. For this purpose, the tool provides a weighted voting schema and an evolutionary algorithm (genetic algorithm) to learn robust weights. Moreover, to test the experiments, the tool includes stratified cross-validation methods which take the collaborative environment into account. In this paper the different collaborative facilities offered by the tool are described. A sample usage of the tool is also provided.

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“…CBR's benefits in solving construction management-related problems over other prediction techniques have been demonstrated by Arditi and Tokdemir ͑1999a,b͒ and Yau and Yang ͑1998͒. Recent research studies about the effectiveness of integrated machine learning approaches indicate that CBR systems could achieve better results when enhanced by other techniques ͑Cardie 1993; Jarmulak and Craw 1999;Jarmulak et al 2000;Ling et al 1997;Shin and Han 2002͒. CBR directly interprets past experiences. In other words, CBR systems predict the outcome of new situations by retrieving previously stored outcomes of similar situations from a case base.…”

Section: Introductionmentioning

confidence: 99%

Using Decision Trees for Determining Attribute Weights in a Case-Based Model of Early Cost Prediction

Doğan

Arditi

Günaydın

2008

J. Constr. Eng. Manage.

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This paper compares the performance of three different decision-tree-based methods of assigning attribute weights to be used in a case-based reasoning ͑CBR͒ prediction model. The generation of the attribute weights is performed by considering the presence, absence, and the positions of the attributes in the decision tree. This process and the development of the CBR simulation model are described in the paper. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of residential building projects. The CBR results indicate that the attribute weights generated by taking into account the information gain of all the attributes performed better than the attribute weights generated by considering only the appearance of attributes in the tree. The study is of benefit primarily to researchers, as it compares the impact of attribute weights generated by three different methods and, hence, highlights the fact that the prediction rate of models such as CBR largely depends on the data associated with the parameters used in the model.

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Genetic Algorithms to Optimise CBR Retrieval

Cited by 39 publications

References 11 publications

Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems

Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems

eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis

Using Decision Trees for Determining Attribute Weights in a Case-Based Model of Early Cost Prediction

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