Assessing similarity between cases by means of fuzzy rules

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of ldquosimilarityrdquo can vary in different situations and remains an issue. This paper proposes a novel similarity model consisting of fuzzy rules to represent the semantics and evaluation criteria for similarity. We believe that fuzzy if-then rules present a more powerful and flexible means to capture domain knowledge for utility oriented similarity modeling than traditional similarity measures based on feature weighting. Fuzzy rule-based reasoning is utilized as a case matching mechanism to determine whether and to which extent a known case in the case library is similar to a given problem in query. Further, we explain that such fuzzy rules for similarity assessment can be learned from the case library. The key to achieving this is pair-wise comparisons of cases with known solutions in the case library such that sufficient training samples can be derived for fuzzy rule learning. The evaluations conducted have shown that the proposed method yields more precise similarity values to approximate case utility than conventional ways of similarity modeling and that fuzzy similarity rules can be learned from a rather small case base without the risk of over-fitting.