Abstract
Assessing the similarity of structured representation of cases in a natural and powerful way is an open issue in case-based reasoning (CBR). In this paper we use the notion of similitude terms, a symbolic representation of structural similarity proposed in an earlier paper. We argue that the issue to be addressed is estimating the relevance of similitude terms with regard to the task at hand, and then we propose a way of using the Case Base to estimate the relevance of similitude terms called the discriminating base. Two specific measures based on Shannon entropy are proposed to assess this relevance: I, the importance of a similitude term, and G, the similitude-based class evidence that estimates class aggregate importance. We show an application of I in the system SPIN for marine sponges identification. A longer version of this paper applies G to two standard Machine Learning datasets for classification tasks.
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Plaza, E., de Mántaras, R.L., Armengol, E. (1996). On the importance of similitude: An entropy-based assessment. In: Smith, I., Faltings, B. (eds) Advances in Case-Based Reasoning. EWCBR 1996. Lecture Notes in Computer Science, vol 1168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020620
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DOI: https://doi.org/10.1007/BFb0020620
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