Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance
This work addresses a specific issue in fuzzy set analysis for automated reasoning systems, but it is incremental as it builds on existing measures without introducing a new paradigm.
The paper tackles the problem of misleading results from independent similarity and distance measures when comparing fuzzy sets, especially in automated reasoning systems, by introducing a combined measure that reduces ambiguity and aids in automatic comparison.
Reasoning with fuzzy sets can be achieved through measures such as similarity and distance. However, these measures can often give misleading results when considered independently, for example giving the same value for two different pairs of fuzzy sets. This is particularly a problem where many fuzzy sets are generated from real data, and while two different measures may be used to automatically compare such fuzzy sets, it is difficult to interpret two different results. This is especially true where a large number of fuzzy sets are being compared as part of a reasoning system. This paper introduces a method for combining the results of multiple measures into a single measure for the purpose of analysing and comparing fuzzy sets. The combined measure alleviates ambiguous results and aids in the automatic comparison of fuzzy sets. The properties of the combined measure are given, and demonstrations are presented with discussions on the advantages over using a single measure.