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

Abstract

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 (FS3VM) has been proposed. The FS3VM 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 FS3VM model, the fundamental theory and methods of the model are discussed and expanded upon, including the FS3VM algorithm, which applies the Sequential Minimal Optimization (SMO) algorithm to the dual problem. The proposed FS3VM model is a smooth and continuous optimization problem, and its dual is a standard quadratic programming. Experimental results demonstrate that the proposed FS3VM model outperforms other compared learning algorithms.