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A least squares twin support vector machine method with uncertain data

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Abstract

Twin support vector machine (TWSVM) learns two nonparallel hyperplanes for binary class classification problems. It assumes that the training data can be collected accurately without any uncertain information. However, in practical applications, the data may contain uncertain information. To deal with the uncertain information, this paper puts forward a novel uncertain-data-based least squares twin support vector machine method (ULSTSVM) which is capable of handling the data uncertainty efficiently. Firstly, since the data may contain uncertain information, a noise vector is introduced to model the uncertain information of each example. Secondly, the noise vectors are incorporated into least squares TWSVM. Finally, to solve the derived learning problem, we employ a two-step heuristic framework which trains the least squares TWSVM classifier and updates the noise vectors alternately. The experimental results have shown that ULSTSVM surpasses the existing robust TWSVM methods in training time and meanwhile achieves a better classification accuracy. In sum, ULSTSVM adopts a noise vector to model the uncertain information and transforms the quadratic programming problems of TWSVM into linear equations, which makes us have a better classification accuracy and higher training efficiency.

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Acknowledgments

The authors would like to thank the reviewers for their very useful comments and suggestions. This work was supported in part by the Natural Science Foundation of China under Grant 61876044 and Grant 62076074, in part by Guangdong Natural Science Foundation under Grant 2020A1515010670 and 2020A1515011501 in part by the Science and Technology Planning Project of Guangzhou under Grant 202002030141.

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Authors and Affiliations

  1. School of Computer, Guangdong University of Technology, Guangzhou, China

    Yanshan Xiao, Jinneng Liu, Kairun Wen, Liang Zhao & Xiangjun Kong

  2. School of Automation, Guangdong University of Technology, Guangzhou, China

    Bo Liu

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  1. Yanshan Xiao
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  2. Jinneng Liu
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  3. Kairun Wen
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  4. Bo Liu
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  5. Liang Zhao
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Correspondence to Jinneng Liu.

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Xiao, Y., Liu, J., Wen, K. et al. A least squares twin support vector machine method with uncertain data. Appl Intell 53, 10668–10684 (2023). https://doi.org/10.1007/s10489-022-03897-3

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