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Improved twin bounded large margin distribution machines for binary classification

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Abstract

Recently, a robust and effective classifier termed twin bounded large margin distribution machine (TBLDM) was suggested. TBLDM is based on the twin bounded support vector machine (TBSVM) and large margin distribution machine (LDM). TBLDM searches for two nonparallel hyperplanes by optimizing the negative and positive margin distributions. Based on TBLDM, this paper suggests a least-square variant of the TBLDM model termed as least squares TBLDM (LSTBLDM) and an iterative variant of TBLDM called iterative TBLDM (ITBLDM). In the first model, i.e., LSTBLDM, the optimization problem contains equality constraints rather than inequality constraints and it solves a system of linear equations, unlike the TBLDM model. In the second model, i.e., ITBLDM, the optimization problems are solved in primal using a simple, functional iterative scheme. The pseudo-codes are also specified for the proposed models to make them easily implementable. The experiments have been performed on one artificial and thirty-eight interesting real-world datasets. The proposed models are compared with the least squares support vector machine (LSSVM), twin support vector machine (TWSVM) and TBLDM. The results based on the classification accuracy reveal the usefulness and applicability of the proposed LSTBLDM and ITBLDM models.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the UCI machine learning repository [32].

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Barenya Bikash Hazarika

  2. Department of Computer Science & Engineering, National Institute of Technology Arunachal Pradesh, Arunachal Pradesh, Jote, India

    Deepak Gupta

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  1. Barenya Bikash Hazarika
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  2. Deepak Gupta
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Correspondence to Deepak Gupta.

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Hazarika, B.B., Gupta, D. Improved twin bounded large margin distribution machines for binary classification. Multimed Tools Appl 82, 13341–13368 (2023). https://doi.org/10.1007/s11042-022-13738-7

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