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Linguistic Vector Similarity Measures and Applications to Linguistic Information Classification

Authors:

Pham Hong Phong,

LeźHoang SonAuthors Info & Claims

International Journal of Intelligent Systems, Volume 32, Issue 1

Pages 67 - 81

https://doi.org/10.1002/int.21830

Published: 01 January 2017 Publication History

Abstract

In this paper, we generalize the similarity degree for linguistic labels to the so-called the linguistic similarity measure. Linguistic vector, whichcan be used to represent objects whose attributes are given in terms of linguistic labels, is defined. Some mathematical properties are stated and proved. The linguistic vector similarity measure is developed and applied to linguistic information classification. Experimental results on real data confirm the effectiveness of the proposed method.

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Peng XSelvachandran G(2019)Pythagorean fuzzy set: state of the art and future directionsArtificial Intelligence Review10.1007/s10462-017-9596-952:3(1873-1927)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10462-017-9596-9
Liu HYang GSuo WGao J(2019)Two‐tuple linguistic utility aggregation operator and its applications to group decision‐makingInternational Journal of Intelligent Systems10.1002/int.2212134:8(1835-1863)Online publication date: 27-May-2019
https://dl.acm.org/doi/10.1002/int.22121
Czarnul P(2018)Parallelization of large vector similarity computations in a hybrid CPU+GPU environmentThe Journal of Supercomputing10.1007/s11227-017-2159-774:2(768-786)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11227-017-2159-7
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Linguistic Vector Similarity Measures and Applications to Linguistic Information Classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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cover image International Journal of Intelligent Systems

International Journal of Intelligent Systems Volume 32, Issue 1

January 2017

103 pages

ISSN:0884-8173

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John Wiley and Sons Ltd.

United Kingdom

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Published: 01 January 2017

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Peng XSelvachandran G(2019)Pythagorean fuzzy set: state of the art and future directionsArtificial Intelligence Review10.1007/s10462-017-9596-952:3(1873-1927)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10462-017-9596-9
Liu HYang GSuo WGao J(2019)Two‐tuple linguistic utility aggregation operator and its applications to group decision‐makingInternational Journal of Intelligent Systems10.1002/int.2212134:8(1835-1863)Online publication date: 27-May-2019
https://dl.acm.org/doi/10.1002/int.22121
Czarnul P(2018)Parallelization of large vector similarity computations in a hybrid CPU+GPU environmentThe Journal of Supercomputing10.1007/s11227-017-2159-774:2(768-786)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11227-017-2159-7
Ngan RAli MSon L(2018)ź-equality of intuitionistic fuzzy setsApplied Intelligence10.1007/s10489-017-0986-048:2(499-525)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s10489-017-0986-0
Hwang CYang MHung W(2018)New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clusteringInternational Journal of Intelligent Systems10.1002/int.2199033:8(1672-1688)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1002/int.21990

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A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets

Cosine similarity measures for intuitionistic fuzzy sets and their applications

Linguistic modelling based on semantic similarity relation among linguistic labels

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