![]() Then we propose a new cosine similarity measure based on the Choquet integral for IFSs. First of all, we give the basic definitions that are used throughout the study, and then we recall the concepts of fuzzy measure and Choquet integral. In this study, we provide a new cosine similarity measure for IFSs by considering the Choquet integral, inspired by a weighted cosine similarity measure for IFSs which has been given by Ye ( 2011). Many studies have been made to solve such a problem and many similarity measures have been proposed by several researchers (see e.g. The purpose of a pattern recognition problem is to decide whether an object belongs to a set or a class. ![]() Also, the Choquet integral model instead of a weighted average model was used to compute this similarity measure. Yang and Ha ( 2008) introduced a similarity measure for IFSs by using a Choquet integral model where a generalized fuzzy measure was used to characterize interactions among elements of IFSs. Actually a fuzzy integral is a generalization of weighted mean. The purpose of the multi criteria decision making field is to order alternatives based on multiple contradictory criteria and choose the best alternative (see e.g. Since that time, fuzzy measures and integrals have been studied on a rather mathematical point of view, especially in multi criteria decision making field. The concepts of fuzzy measure (or capacity or non-additive measure) and the Choquet integral were introduced by Choquet ( 1953). The concept of cosine similarity measure for fuzzy sets is defined as the inner product of two vectors divided by the product of their lengths, that is, the cosine of the angle between the vector representations of fuzzy sets and it has been investigated by many researchers (Gerstenkorn and Manko, 1991 Ye, 2011, 2016 Garg, 2018 Wei, 2018 Wei and Wei, 2018 Wei et al., 2019 Wang et al., 2019). Many similarity measures of IFSs have been investigated in the literature and the concept of cosine similarity measure is one of them. A similarity measure is an important tool for measuring the degree of similarity between two IFSs and various versions of the concept of the similarity measure have been applied to various fields such as pattern recognition, medical diagnosis, decision making, face recognition systems, clustering (see e.g. The concept of similarity measure is one of these study areas. 2013 Balasubramaniam and Ananthi, 2014 Mani and Jerome, 2014 Lu and Ye, 2016 Das et al., 2016 Luo and Zhao, 2018). The theory of intuitionistic fuzzy sets has been extensively studied by many authors (see e.g. It would be really helpful if this can be done.Zadeh ( 1965) introduced the concept of fuzzy set by using a membership function and Atanassov ( 1986) expanded this concept to the concept of intuitionistic fuzzy set (IFS) by using both a membership function and a non-membership function. I was thinking if i can load this file in Knime to then calculate cosine similarity etc. Say, for eg: below are the columns (separated by ‘|’ ) of that sentence-embedded csv file (not displayed well. Couldn’t find any.īut if there’s a node in Knime where I can load vectors of a pre-trained model’s sentence embedding, it would be great. Additionally, I checked (many) nodes to see if I can add a sentence context to the embedding.
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