Three-way Imbalanced Learning based on Fuzzy Twin SVM
This work addresses imbalanced classification, a common issue in machine learning, by integrating three-way decision with SVM, though it appears incremental as it builds on existing SVM and fuzzy methods.
The authors tackled imbalanced classification problems by combining three-way decision with SVM, proposing a new fuzzy twin SVM with three-way membership (TWFTSVM) that assigns higher fuzzy membership to minority samples. The model was tested on 47 datasets with varying imbalance ratios and significantly outperformed other traditional SVM-based methods.
Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. However, three-way decision is rarely combined with the currently popular field of machine learning to expand its research. In this paper, three-way decision is connected with SVM, a standard binary classification model in machine learning, for solving imbalanced classification problems that SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy twin support vector machine with three-way membership (TWFTSVM) are proposed. The new three-way fuzzy membership function is defined to increase the certainty of uncertain data in both input space and feature space, which assigns higher fuzzy membership to minority samples compared with majority samples. To evaluate the effectiveness of the proposed model, comparative experiments are designed for forty-seven different datasets with varying imbalance ratios. In addition, datasets with different imbalance ratios are derived from the same dataset to further assess the proposed model's performance. The results show that the proposed model significantly outperforms other traditional SVM-based methods.