Hossein Moosaei, Fatemeh Bazikar, and Milan Hladík. Multi-task twin support vector machine with Universum data. Eng. Appl. Artif. Intell., 132:107951:1–15, 2024.
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Multi-task learning (MTL) has emerged as a promising topic of machine learning in recent years, aiming to enhance the performance of numerous related learning tasks by exploiting beneficial information. Traditionally, during the training phase, existing multi-task learning models focused solely on the data related to the target task. In our approach, Universum data, which does not belong to any class in the classification problem but belongs to the same domain as the target data, is incorporated into classifier training as prior knowledge. This study looks at the challenge of multi-task learning using Universum data to employ non-target task data, which leads to better performance. It proposes a multi-task twin support vector machine with Universum data (UMTSVM) and provides two approaches to its solution. The first approach takes into account the dual formulation of UMTSVM and tries to solve a quadratic programming problem. The second approach formulates a least-squares version of UMTSVM and refers to it as LS-UMTSVM to further increase the generalization performance. The solution of the two primal problems in LS-UMTSVM is simplified to solving just two systems of linear equations, resulting in an incredibly simple and quick approach. Numerical experiments on several popular multi-task data sets and medical data sets demonstrate the efficiency of the proposed methods.
@article{MooBaz2024a,
author = "Hossein Moosaei and Fatemeh Bazikar and Milan Hlad\'{\i}k",
title = "Multi-task twin support vector machine with {Universum} data",
journal = "Eng. Appl. Artif. Intell.",
fjournal = "Engineering Applications of Artificial Intelligence",
volume = "132",
pages = "107951:1-15",
year = "2024",
doi = "10.1016/j.engappai.2024.107951",
issn = "0952-1976",
url = "https://www.sciencedirect.com/science/article/pii/S095219762400109X",
bib2html_dl_html = "https://doi.org/10.1016/j.engappai.2024.107951",
abstract = "Multi-task learning (MTL) has emerged as a promising topic of machine learning in recent years, aiming to enhance the performance of numerous related learning tasks by exploiting beneficial information. Traditionally, during the training phase, existing multi-task learning models focused solely on the data related to the target task. In our approach, Universum data, which does not belong to any class in the classification problem but belongs to the same domain as the target data, is incorporated into classifier training as prior knowledge. This study looks at the challenge of multi-task learning using Universum data to employ non-target task data, which leads to better performance. It proposes a multi-task twin support vector machine with Universum data (UMTSVM) and provides two approaches to its solution. The first approach takes into account the dual formulation of UMTSVM and tries to solve a quadratic programming problem. The second approach formulates a least-squares version of UMTSVM and refers to it as LS-UMTSVM to further increase the generalization performance. The solution of the two primal problems in LS-UMTSVM is simplified to solving just two systems of linear equations, resulting in an incredibly simple and quick approach. Numerical experiments on several popular multi-task data sets and medical data sets demonstrate the efficiency of the proposed methods.",
keywords = "Multi-task learning; Universum; Twin support vector machine; Dual problem; Least-squares",
}
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