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- W4385350423 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 (FS 3 VM) has been proposed. The FS 3 VM 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 FS 3 VM model, the fundamental theory and methods of the model are discussed and expanded upon, including the FS 3 VM algorithm, which applies the Sequential Minimal Optimization (SMO) algorithm to the dual problem. The proposed FS 3 VM model is a smooth and continuous optimization problem, and its dual is a standard quadratic programming. Experimental results demonstrate that the proposed FS 3 VM model outperforms other compared learning algorithms." @default.
- W4385350423 created "2023-07-29" @default.
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- W4385350423 date "2023-08-30" @default.
- W4385350423 modified "2023-09-24" @default.
- W4385350423 title "A classification method of fuzzy semi-supervised support vector machines for the problems of imbalance" @default.
- W4385350423 doi "https://doi.org/10.1142/s0219691323500388" @default.
- W4385350423 hasPublicationYear "2023" @default.
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