A New Similarity Measure Between Intuitionistic Fuzzy Sets Based on a Choquet Integral Model
ABSTRACT Several existing similarity measures between intuitionistic fuzzy sets (IFSs) and between vague sets are reviewed. A numerical example shows that these similarity measures are not always reasonable in some cases, and one reason is that inherent interactions among elements of a given universe are ignored. To overcome the drawbacks of these similarity measures, a new similarity measure of IFSs is proposed based on a Choquet integral model, where a generalized fuzzy measure is used to characterize interactions among elements of a given universe of IFSs or vague sets, and the Choquet integral model instead of a weighted average model is used to compute the new similarity measure. Further, properties of the new similarity measure are discussed, and numerical examples show that this new similarity measure is more reasonable than the existing similarity measures.
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ABSTRACT: One similarity measurement strategy for bio-signal waveform is presented in this paper. A tunnel morph is introduced to describe the waveform. It measures the waveform not only by a value, but considers the curve feature of waveforms. Then a set of integrity definitions are presented in the paper. It contains segmentation, measurement function selection, tunnel morph width, and best partition point assigning. A case study on AECG (Ambulatory Electrocardiogram) is also presented to give a proof for the similarity measurement strategy. Through experiment, the sensitivity and positive prediction of the strategy is higher than city block, Euclidean distance and correlation coefficient. Data used in the paper is from MIT/BIH.Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on; 11/2010