Author(s)

XiaoXuan Hao¹, ChenCheng Liu², XiaoDong Pan^1*

Affiliation(s)

¹School of Mathematics, Southwest Jiaotong University, Chengdu 611756, Sichuan, China.

²School of Computer Science and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, Sichuan, China.

Corresponding Author

XiaoDong Pan

ABSTRACT

In this paper, challenges in existing similarity measures for interval-valued picture fuzzy sets are addressed, including high computational complexity, parameter dependency, and failure to satisfy similarity axioms. First, the fundamental concepts of interval-valued picture fuzzy sets are introduced, along with definitions of distance measure, entropy measure, and similarity measure. Then, by representing interval-valued picture fuzzy sets as tetrahedrons in a 3D coordinate system and selecting 11 representative boundary points, novel entropy and similarity measures are developed based on the distances among these points. Finally, numerical examples are provided to demonstrate the effectiveness and practicality of the proposed measures, which are successfully applied to pattern recognition. Compared with existing measures, the proposed entropy and similarity measures exhibit three advantages: simplified computational procedures, reduced parameter dependency, and strict adherence to similarity axioms.

KEYWORDS

Interval-valued picture fuzzy set; Entropy measure; Similarity measure; Pattern recognition

CITE THIS PAPER

XiaoXuan Hao, ChenCheng Liu, XiaoDong Pan. A novel similarity measure based on entropy of interval-valued picture fuzzy set and its application in pattern recognition. World Journal of Information Technology. 2025, 3(5): 34-40. DOI: https://doi.org/10.61784/wjit3065.

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