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dc.contributor.authorAdtha Lawanna
dc.date.accessioned2018-06-14T02:41:14Z
dc.date.available2018-06-14T02:41:14Z
dc.date.issued2015-11
dc.identifier.citation1st Intelligent Informatics and Biomedical Sciences, ICIIBMS-2015, pp. 129-134. (November 28 – 30, 2015)en_US
dc.identifier.urihttp://repository.au.edu/handle/6623004553/21056
dc.description.abstractCase-based reasoning systems have been applied for machine learning, artificial intelligence, knowledge-based systems and other related fields in order to provide the right solution to the right problem regarding the four processes, which are the process to retrieve, reuse, revise, and retain cases. This paper focuses on the last process because it produces two main problems, which are the size of a case base increase and the ability of preserving the competency decreases. These critical issues are occurring when repeating the cycles of case-based reasoning. Consequently, the case-based maintenance methods are developed to handle the situations. Accordingly, this paper proposes an effective model for case-based maintenance in casebased reasoning systems to give the best results compared with random, utility, footprint, footprint and utility deletion including case addition algorithm. By running the seven comparative studies on ten datasets retrieved from the machine learning repository, especially to study the efficiency of each algorithm in terms of reducing the size of the case base by selecting the small number of case solutions and preserving the competency after the maintenance systems are applied. According to experimental results, the effectiveness of the proposed model for storing the number of case solution gives the lower size of a case base, when compared with the existing techniques about 34.34%-114.84%. Besides, the percentage of adapting solutions for the traditional methods are lower than the proposed model as about 1.12-6.64 times, including the percent solving problem is lower than the effective model approximately 4.73%-33.55%.en_US
dc.format.extent6 pagesen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoengen_US
dc.subjectCase-based reasoningen_US
dc.subjectCase-based maintenanceen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDeletionen_US
dc.subjectAdditionen_US
dc.titleAn Effective Model for Case-Based Maintenance in Cased-Based Reasoning Systemsen_US
dc.typeTexten_US
dc.rights.holderAdtha Lawannaen_US
mods.genreConference Paperen_US
au.link.externalLink[Full Text] (http://ieeexplore.ieee.org/document/7439505/?reload=true)


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