Gaussian Aggregation Operators and Applications to Intuitionistic Fuzzy Classification

ÜNVER M.

Journal of Classification, vol.42, no.3, pp.596-623, 2025 (SCI-Expanded, SSCI, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 3
  • Publication Date: 2025
  • Doi Number: 10.1007/s00357-025-09507-4
  • Journal Name: Journal of Classification
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, BIOSIS, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), zbMATH, DIALNET
  • Page Numbers: pp.596-623
  • Keywords: Aggregation operator, Classification, Gaussian triangular-norm, Intuitionistic fuzzy set
  • Ankara University Affiliated: Yes

Abstract

In this study, we introduce novel aggregation operators for intuitionistic fuzzy values based on the Gaussian error function. We define the Gaussian triangular-norm and triangular-conorm operations using an Archimedean framework and propose the Gaussian weighted arithmetic (GWA) and the Gaussian weighted geometric (GWG) aggregation operators. These operators are applied to the classification of the Genus Iris dataset, using an improved cosine similarity measure and fuzzy classification algorithms. We demonstrate the effectiveness of these methods in handling uncertainty and improving classification accuracy. Our experimental results show that the GWA and GWG aggregation operators achieve superior performance, particularly in distinguishing between closely related species, with accuracy metrics surpassing some previous methods. This work highlights the utility of Gaussian-based fuzzy logic in complex classification tasks, offering insights into improving machine learning models dealing with imprecise data.