A classification method of fuzzy semi-supervised support vector machines for the problems of imbalance

Positive instances are often significantly less than negative instances in real-world classification problems. However, positive categories are typically more relevant to the primary focus of categorization tasks. Moreover, obtaining labeled data is often expensive, and the majority of real-life data is unlabeled. Therefore, semi-supervised learning has become a popular approach for addressing imbalanced problems. Traditional support vector machines (SVMs) treat all samples equally and are not suitable for semi-supervised learning. To address this issue, a semi-supervised model called the fuzzy semi-supervised SVM ((FSVM)-V-3) has been proposed. The (FSVM)-V-3 model uses the degree of entropy-based fuzzy membership to ensure the materiality of positive classes by assigning positive instances to relatively large degrees of fuzzy membership. After introducing the mainstream (FSVM)-V-3 model, the fundamental theory and methods of the model are discussed and expanded upon, including the (FSVM)-V-3 algorithm, which applies the Sequential Minimal Optimization (SMO) algorithm to the dual problem. The proposed (FSVM)-V-3 model is a smooth and continuous optimization problem, and its dual is a standard quadratic programming. Experimental results demonstrate that the proposed (FSVM)-V-3 model outperforms other compared learning algorithms.