Research and Development in Intelligent Systems XXVIII 2011
DOI: 10.1007/978-1-4471-2318-7_12
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Choosing a Case Base Maintenance Algorithm using a Meta-Case Base

Abstract: In Case-Based Reasoning (CBR), case base maintenance algorithms remove noisy or redundant cases from case bases. The best maintenance algorithm to use on a particular case base at a particular stage in a CBR system's lifetime will vary. In this paper, we propose a meta-case-based classifier for selecting the best maintenance algorithm. The classifier takes in a description of a case base that is to undergo maintenance, and uses meta-cases-descriptions of case bases that have undergone maintenance-to predict th… Show more

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Cited by 7 publications
(2 citation statements)
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“…Even though some of the algorithm performances may be described by the decision tree rules, self-organizing map visualisations showed that there are clusters of datasets whose performance was not easily explained by the chosen characteristics. One of the key difficulties in meta-learning is still creating features that accurately describe a dataset and reflect the variety of issue complexity [4]. This study suggests a meta-case-based classifier for choosing the optimum Case-Based Reasoning maintenance method (CBR).…”
Section: Literature Surveymentioning
confidence: 99%
“…Even though some of the algorithm performances may be described by the decision tree rules, self-organizing map visualisations showed that there are clusters of datasets whose performance was not easily explained by the chosen characteristics. One of the key difficulties in meta-learning is still creating features that accurately describe a dataset and reflect the variety of issue complexity [4]. This study suggests a meta-case-based classifier for choosing the optimum Case-Based Reasoning maintenance method (CBR).…”
Section: Literature Surveymentioning
confidence: 99%
“…Although Ho and Basu's metrics were designed for two-class problems, some researchers generalize it to more than two classes by averaging measures obtained between all possible pair of classes [37,41]. Also, although recent generalizations of the Ho-Basu c-measures have been proposed [2,12,41], none addresses explicitly the problem of classifying large image datasets.…”
Section: Previous Workmentioning
confidence: 99%