Abstract
Case-Based Reasoning (CBR) is a good technique to solve new
problems based in previous experience. Main assumption in CBR
relies in the hypothesis that similar problems should have
similar solutions. CBR systems retrieve the most similar cases or
experiences among those stored in the Case Base. Then, previous
solutions given to these most similar past-solved cases can be
adapted to fit new solutions for new cases or problems in a
particular domain, instead of derive them from scratch. Thus,
similarity measures are key elements in obtaining reliable
similar cases, which will be used to derive solutions for new
cases. This paper describes a comparative analysis of several
commonly used similarity measures, including a measure previously
developed by the authors, and a study on its performance in the
CBR retrieval step for feature-vector case representations. The
testing has been done using sixteen data sets from the UCI
Machine Learning Database Repository, plus two complex
environmental databases.
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