Revistes Catalanes amb Accés Obert (RACO)

Rule-based fuzzy object similarity

Horst Bunke, Xavier Fàbregas, Abraham Kandel


A new similarity measure for objects that are represented by
feature vectors of fixed dimension is introduced. It can
simultaneously deal with numeric and symbolic features.
Also, it can tolerate missing feature values. The similarity measure between
two objects is described in terms of the similarity of their
features. IF-THEN rules are being used to model the
individual contribution of each feature to the global similarity measure
between a pair
of objects. The proposed similarity measure is based on
fuzzy sets and this allows us to deal with vague, uncertain and distorted
information in a natural way. Several formal properties of the
proposed similarity measure are derived; in particular, we show that the measure can be used to model the Euclidean
distance as well as other, non-Euclidean distance functions. Also, an
application of the proposed similarity measure to
nearest-neighbor classification in a medical expert system is

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