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Angle-based twin support vector machine

Author

Listed:
  • Reshma Khemchandani

    (South Asian University)

  • Pooja Saigal

    (South Asian University)

  • Suresh Chandra

    (Indian Institute of Technology, Delhi)

Abstract

In this paper, a novel nonparallel hyperplane based classifier termed as “angle-based twin support vector machine” (ATWSVM) has been proposed which is motivated by the concept of twin support vector machine (TWSVM). TWSVM obtains two nonparallel hyperplanes by solving a pair of quadratic programming problems (QPPs). ATWSVM presents a generic classification model, where the first problem can be formulated using a TWSVM-based classifier and the second problem is an unconstrained minimization problem (UMP) which is reduced to solving a system of linear equations. The second hyperplane is determined so that it is proximal to its own class and the angle between the normals to the two hyperplanes is maximized. The notion of angle has been introduced to have maximum separation between the two hyperplanes. In this work, we have presented two versions of ATWSVM: one that solves a QPP and a UMP; second which formulates both the problems as UMPs. The training time of ATWSVM is much less than that of TWSVM because ATWSVM solves the second problem as UMP instead of QPP. To test the efficacy of the proposed classifier, experiments have been conducted on synthetic and benchmark datasets and it is observed that the proposed classifier achieves classification accuracy comparable or better than that of TWSVM. This work also proposes application of ATWSVM for color image segmentation.

Suggested Citation

  • Reshma Khemchandani & Pooja Saigal & Suresh Chandra, 2018. "Angle-based twin support vector machine," Annals of Operations Research, Springer, vol. 269(1), pages 387-417, October.
    • Handle: RePEc:spr:annopr:v:269:y:2018:i:1:d:10.1007_s10479-017-2604-2
    DOI: 10.1007/s10479-017-2604-2
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    References listed on IDEAS

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    1. Yitian Xu & Mei Chen & Guohui Li, 2016. "Least squares twin support vector machine with Universum data for classification," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(15), pages 3637-3645, November.
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    Cited by:

    1. M. A. Ganaie & M. Tanveer & Jatin Jangir, 2023. "EEG signal classification via pinball universum twin support vector machine," Annals of Operations Research, Springer, vol. 328(1), pages 451-492, September.
    2. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.

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    1. M. A. Ganaie & M. Tanveer & Jatin Jangir, 2023. "EEG signal classification via pinball universum twin support vector machine," Annals of Operations Research, Springer, vol. 328(1), pages 451-492, September.

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