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A rough margin-based multi-task v-twin support vector machine for pattern classification
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DOI：10.1016/j.asoc.2021.107769.png)
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Twin support vector machine (TSVM) has attracted significant attention in recent years, but it is suitable for solving the single-task learning (STL) problems. It trains each task independently and neglects the relationships among all tasks. Conversely, multi-task learning (MTL) explores the shared information between multiple correlated tasks, which obtains a better classifier than STL. Nevertheless, the existing multi-task twin support vector machines give the same penalties to the misclassified samples. In fact, the misclassified samples play a different role in generating separating hyperplane. Motivated by above studies, we put forward a rough margin-based multi-task v-twin support vector machine (rough MT-v-TSVM) in this paper. The proposed rough MT-v-TSVM gives different penalties to the misclassified samples depending on their positions. It not only takes full advantage of rough v-TSVM, but also discovers the commonality among tasks and maintains the individuality of each task. Therefore, compared with the state-of-the-art algorithms, our method yields better classification performance. In addition, we apply it to Chinese wine dataset to verify the effectiveness. Finally, the related extensions are further discussed, especially a fast SMO-type decomposition method (SDM) is introduced to handle relatively large-scale problems for acceleration. Comprehensive experiments are conducted on eleven benchmark datasets and an image dataset. The results demonstrate that our proposed algorithm can avoid over-fitting and achieve better classification accuracy, meanwhile it does not increase computational time compared with DMTSVM and MT-v-TSVM. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Multi-task learning
Twin support vector machine
Rough v-twin support vector machine
Pattern classification
SMO
期刊
IF:
6.6
论文数:
1.4W
被引数:
5.7W
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