Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers - Georgios Charizanos; Haydar Demirhan; Duygu İçen (Intelligent Systems with Applications)
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.