A Dynamic Interval-Valued Intuitionistic Fuzzy Sets Applied to Pattern Recognition

Abstract

We present dynamic interval-valued intuitionistic fuzzy sets (DIVIFS), which can improve the recognition accuracy when they are applied to pattern recognition. By analyzing the degree of hesitancy, we propose some DIVIFS models from intuitionistic fuzzy sets (IFS) and interval-valued IFS (IVIFS). And then we present a novel ranking condition on the distance of IFS and IVIFS and introduce some distance measures of DIVIFS satisfying the ranking condition. Finally, a pattern recognition example applied to medical diagnosis decision making is given to demonstrate the application of DIVIFS and its distances. The simulation results show that the DIVIFS method is more comprehensive and flexible than the IFS method and the IVIFS method.

A New Similarity Measure of Interval‐Valued Intuitionistic Fuzzy Sets Considering Its Hesitancy Degree and Applications in Expert Systems

“…Example 18 (see [8]). In order to strengthen academic education and promote the quality of teaching, the school of management of a Chinese university wants to introduce oversea outstanding professor.…”

“…Due to the high complexity of the real world and the limitation of people's judgment, the use of interval-valued intuitionistic fuzzy set has gained much attention [2][3][4][5][6][7][8][9]. As one of the most important applications, the expert system to solve problems under fuzzy environment is a hot research topic [10][11][12][13][14][15].…”

A Novel Weighted Average Index Method of Interval Valued Intuitionistic Fuzzy Sets and Its Application to Outsourced Software Project Risk Assessment

“…In 1980s, Atanassov [1,2] introduced membership function, non-membership function and hesitancy function, and presented intuitionistic fuzzy sets (IFS) and interval valued intuitionistic fuzzy sets (IVIFS), which generalized the FS theory. In the research field of IFS and IVIFS, Yager [3] discussed its characteristics, more scholars applied it to decision making [4][5][6][7][8][9] and pattern recognition [10]. Though many scholars studied IFS and IVIFS, few references related to the study of outsourced software project risk assessment based on IFS and IVIFS were proposed.…”

An approach to intuitionistic fuzzy multiple attribute decision making based on Maclaurin symmetric mean operators

“…Aggregation operator is the most useful tool for information fusion, which plays an important role in information science and has become important due to the significant applications in diverse domains like decision making [1][2][3][4], pattern recognition [5], machine learning [6], image processing [7] etc. Many authors proposed a lot of aggregation operators for intuitionistic fuzzy set [8][9][10][11], hesitant fuzzy set [12][13][14], and type-2 fuzzy set [15,16] during the last decades.…”