Fuzzy-weighted distance and its applications in pattern recognition and classification

摘要

The fuzzy-weighted distance presented shows that every feature of every sample has a different effect on distance in pattern recognition and classification. The difference is described by using the concept of feature odds defined by the authors, which is derived from fuzzy set theory and is determined by human thinking in object recognition. The reasonableness of the proposed distance is discussed. Experimental results are presented that show the advantages of this distance.