An Effective Model for Case-Based Maintenance in Cased-Based Reasoning Systems
An Effective Model for Case-Based Maintenance in Cased-Based Reasoning Systems
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2015-11
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eng
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6 pages
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1st Intelligent Informatics and Biomedical Sciences, ICIIBMS-2015, pp. 129-134. (November 28 – 30, 2015)
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Abstract
Case-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%.