Intuitionistic fuzzy twin support vector machines for imbalanced data

The classification of imbalanced datasets is one of the main challenges in machine learning techniques. Support vector machine (SVM), which generates a model biased within the majority class, usually has bad performance on the minority class because this class may be considered incorrectly as noises. Moreover, datasets often include noises and outliers, and SVM cannot effectively deal with those datasets. In this paper, to defeat the aforementioned challenges, we propose intuitionistic fuzzy twin support vector machines for imbalanced data (IFTSVM-ID). The proposed method can easily handle imbalanced datasets in the presence of noises and outliers. A reasonable weighting strategy is offered to deal with imbalanced classes, and a margin-based technique is assigned to reduce the impact of noise and outliers. We formulate the linear and non-linear kernel functions to find two non-parallel hyperplanes. One realworld and thirty-two imbalanced datasets are selected to validate the performance of IFTSVM-ID. The Friedman test and the bootstrap technique with 95% confidence interval are applied to quantify the results statistically. The experimental results show that our proposed method has much better performance in comparison with other similar techniques. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, the authors propose a method called intuitionistic fuzzy twin support vector machines for imbalanced data (IFTSVM-ID) to address the challenges of imbalanced datasets. The method effectively handles noise and outliers, and uses a weighting strategy and margin-based technique to deal with imbalanced classes. Experimental results demonstrate that the proposed method outperforms other similar techniques.