Affiliations: [a] School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, PR China | [b] School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, Shaanxi, PR China
Correspondence:
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Corresponding author. Yongming Li, School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi 710062, PR China. E-mail: [email protected].
Abstract: An essential topic of interval-valued intuitionistic fuzzy sets(IVIFSs) is distance measures. In this paper, we introduce a new kind of distance measures on IVIFSs. The novelty of our method lies in that we consider the width of intervals so that the uncertainty of outputs is strongly associated with the uncertainty of inputs. In addition, better than the distance measures given by predecessors, we define a new quaternary function on IVIFSs to construct the above-mentioned distance measures, which called interval-valued intuitionistic fuzzy dissimilarity function. Two specific methods for building the quaternary functions are proposed. Moreover, we also analyzed the degradation of the distance measures in this paper, and show that our measures can perfectly cover the measures on a simpler set. Finally, we provide illustrative examples in pattern recognition and medical diagnosis problems to confirm the effectiveness and advantages of the proposed distance measures.
