دورية أكاديمية
MTKSVCR: A novel multi-task multi-class support vector machine with safe acceleration rule.
| العنوان: | MTKSVCR: A novel multi-task multi-class support vector machine with safe acceleration rule. |
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| المؤلفون: | Pang X; School of Mathematics and Statistics, Qingdao University, Qingdao 266071, China., Xu C; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China., Xu Y; College of Science, China Agricultural University, Beijing 100083, China. Electronic address: xytshuxue@126.com. |
| المصدر: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Jul; Vol. 175, pp. 106317. Date of Electronic Publication: 2024 Apr 12. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York : Pergamon Press, [c1988- |
| مواضيع طبية MeSH: | Support Vector Machine*, Humans ; Algorithms |
| مستخلص: | Regularized multi-task learning (RMTL) has shown good performance in tackling multi-task binary problems. Although RMTL can be used to handle multi-class problems based on "one-versus-one" and "one-versus-rest" techniques, the information of the samples is not fully utilized and the class imbalance problem occurs. Motivated by the regularization technique in RMTL, we propose an original multi-task multi-class model termed MTKSVCR based on "one-versus-one-versus-rest" strategy to achieve better testing accuracy. Due to the utilization of the idea of RMTL, the related information included in multiple tasks is mined by setting different penalty parameters before task-common and task-specific regularization terms. However, the proposed MTKSVCR is time-consuming since it employs all samples in each optimization problem. Therefore, a multi-parameter safe acceleration rule termed SA is further presented to reduce the time consumption. It identifies and deletes most of the superfluous samples corresponding to 0 elements in the dual optimal solution before solving. Then, only a reduced dual problem is to be solved and the computational efficiency is improved accordingly. The biggest advantage of the proposed SA lies in safety. Namely, it derives an identical optimal solution to the primal problem without SA. In addition, our method remains effective when multiple parameters change simultaneously. Experiments on different artificial datasets and benchmark datasets verify the validity of the proposed methods. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 Elsevier Ltd. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Multi-class; Multi-task; Safe screening; Speedup; Support vector machine |
| تواريخ الأحداث: | Date Created: 20240419 Date Completed: 20240510 Latest Revision: 20240510 |
| رمز التحديث: | 20240511 |
| DOI: | 10.1016/j.neunet.2024.106317 |
| PMID: | 38640699 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2024.106317 |