Abstract
Building a case base for a case-based reasoning (CBR) system is incomplete without similarity measures. For the attribute-value case structure similarity between values of an attribute should logically fit their relationship. Bipartite graphs have been shown to be a good representation of relationships between values of symbolic attributes and the diagnosis of the cases in a technical diagnosis CBR system, while using an asymmetrical weighted one-mode projection on the values to model their similarity.
However, the weighted one-mode projection assumes that the set of symbols is static, which is contradictory to the dynamic nature of case bases as defined by the retain phase of the CBR cycle. In this work we present two methods to update the similarity measure whenever new information is available and compare them. We show that even though updating the similarity measure to exactly reflect the case base had the new information been available a-priori produces better results, an imperfect update is a feasible, less time consuming temporary solution.
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Stram, R., Reuss, P., Althoff, KD. (2018). Dynamic Case Bases and the Asymmetrical Weighted One-Mode Projection. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_26
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