Work

A Novel Distance Measure of Bipolar Neutrosophic Sets with an Application in Pattern Classification

Year: 2025

Type: article

Abstract: A bipolar neutrosophic set (BNS) is designed to handle uncertain information by capturing both supportive and opposing aspects of data. In this paper, the pattern classification method is studied based on the proposed Hamming–Chebyshev hybrid distance measure (HCHDM). First, the HCHDM of the bipolar neutrosophic sets is proposed that not only captures discrete differences, but also reflects the maximum dimensional deviation in a more complex environment. Then, the axiomatic definition of the distance measure is proved and some examples are given to show it can better discriminate between the differences of BNSs. Based on the distance measure, an algorithm to solve the pattern classification problem is given. The numerical examples show that the proposed distance measure method is effective in solving pattern classification problems.

Source: Symmetry

DOI: 10.3390/sym17122105

Author: Chunfang Liu

Institution: Shenyang Aerospace University

Language: English

Topic: Fuzzy and Soft Set Theory

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Shenyang Aerospace University