Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers
Binary classification is a critical task in pattern recognition applications in artificial intelligence and machine learning. The main weakness of binary classifiers is their sensitivity towards the imbalance in the number of observations in the binary classes and separation by a subset of features....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000201 |
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| author | Georgios Charizanos Haydar Demirhan Duygu İçen |
| author_facet | Georgios Charizanos Haydar Demirhan Duygu İçen |
| author_sort | Georgios Charizanos |
| collection | DOAJ |
| description | Binary classification is a critical task in pattern recognition applications in artificial intelligence and machine learning. The main weakness of binary classifiers is their sensitivity towards the imbalance in the number of observations in the binary classes and separation by a subset of features. Although various robust approaches are introduced against these issues, they need prolonged runtimes, limiting their applicability in artificial intelligence applications or for large datasets. In this study, we introduce a new binary classification framework called the fuzzy-Bayesian logistic regression, which incorporates robust Bayesian logistic regression with fuzzy classification using Gaussian fuzzy numbers. The proposed method improves classification performance while providing significant gains in computation time. We benchmark the proposed method with eight fuzzy, Bayesian, and machine learning classifiers using seventeen datasets. The results indicate that the fuzzy-Bayesian logistic regression outperforms all benchmark methods across all datasets in terms of six performance indicators. Moreover, the proposed method is shown to be significantly more efficient than its closest competitor, improving computational efficiency. The proposed method provides a promising binary classifier for a wide range of applications with its computational efficiency and robustness towards imbalance and separation issues in the data. |
| format | Article |
| id | doaj-art-20aabe5e4bb74525a873dc247156a3ff |
| institution | DOAJ |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-20aabe5e4bb74525a873dc247156a3ff2025-08-20T02:54:50ZengElsevierIntelligent Systems with Applications2667-30532025-06-012620049410.1016/j.iswa.2025.200494Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbersGeorgios Charizanos0Haydar Demirhan1Duygu İçen2RMIT University, Mathematical Sciences, School of Science, Melbourne, 3000, AustraliaRMIT University, Mathematical Sciences, School of Science, Melbourne, 3000, Australia; Corresponding author.Hacettepe University, Department of Statistics, Ankara, 06800, TurkeyBinary classification is a critical task in pattern recognition applications in artificial intelligence and machine learning. The main weakness of binary classifiers is their sensitivity towards the imbalance in the number of observations in the binary classes and separation by a subset of features. Although various robust approaches are introduced against these issues, they need prolonged runtimes, limiting their applicability in artificial intelligence applications or for large datasets. In this study, we introduce a new binary classification framework called the fuzzy-Bayesian logistic regression, which incorporates robust Bayesian logistic regression with fuzzy classification using Gaussian fuzzy numbers. The proposed method improves classification performance while providing significant gains in computation time. We benchmark the proposed method with eight fuzzy, Bayesian, and machine learning classifiers using seventeen datasets. The results indicate that the fuzzy-Bayesian logistic regression outperforms all benchmark methods across all datasets in terms of six performance indicators. Moreover, the proposed method is shown to be significantly more efficient than its closest competitor, improving computational efficiency. The proposed method provides a promising binary classifier for a wide range of applications with its computational efficiency and robustness towards imbalance and separation issues in the data.http://www.sciencedirect.com/science/article/pii/S2667305325000201Binary responseBayesian inferenceLogistic regressionFuzzy numbersSeparationImbalance |
| spellingShingle | Georgios Charizanos Haydar Demirhan Duygu İçen Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers Intelligent Systems with Applications Binary response Bayesian inference Logistic regression Fuzzy numbers Separation Imbalance |
| title | Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers |
| title_full | Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers |
| title_fullStr | Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers |
| title_full_unstemmed | Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers |
| title_short | Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers |
| title_sort | binary classification with fuzzy bayesian logistic regression using gaussian fuzzy numbers |
| topic | Binary response Bayesian inference Logistic regression Fuzzy numbers Separation Imbalance |
| url | http://www.sciencedirect.com/science/article/pii/S2667305325000201 |
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