2001 | OriginalPaper | Buchkapitel
Rough Sets Reduction Techniques for Case-Based Reasoning
verfasst von : Maria Salamó, Elisabet Golobardes
Erschienen in: Case-Based Reasoning Research and Development
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Case Based Reasoning systems are often faced with the problem of deciding which instances should be stored in the case base. An accurate selection of the best cases could avoid the system being sensitive to noise, having a large memory storage requirements and, having a slow execution speed. This paper proposes two reduction techniques based on Rough Sets theory: Accuracy Rough Sets Case Memory (AccurCM) and Class Rough Sets Case Memory (ClassCM). Both techniques reduce the case base by analysing the representativity of each case of the initial case base and applying a different policy to select the best set of cases. The first one extracts the degree of completeness of our knowledge. The second one obtains the quality of approximation of each case. Experiments using different domains, most of them from the UCI repository, show that the reduction techniques maintain accuracy obtained when not using them. The results obtained are compared with those obtained using well-known reduction techniques.