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KNN-based least squares twin support vector machine for pattern classification

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Abstract

The least squares twin support vector machine (LSTSVM) generates two non-parallel hyperplanes by directly solving a pair of linear equations as opposed to solving two quadratic programming problems (QPPs) in the conventional twin support vector machine (TSVM), which makes learning speed of LSTSVM faster than that of the TSVM. However, LSTSVM fails to discover underlying similarity information within samples which may be important for classification performance. To address the above problem, we apply the similarity information of samples into LSTSVM to build a novel non-parallel plane classifier, called K-nearest neighbor based least squares twin support vector machine (KNN-LSTSVM). The proposed method not only retains the superior advantage of LSTSVM which is simple and fast algorithm but also incorporates the inter-class and intra-class graphs into the model to improve classification accuracy and generalization ability. The experimental results on several synthetic as well as benchmark datasets demonstrate the efficiency of our proposed method. Finally, we further went on to investigate the effectiveness of our classifier for human action recognition application.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets.html

  2. The Cython is a superset of the Python programming language and generates efficient C code. More info at http://cython.org/

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Acknowledgements

We gratefully thank the anonymous reviewers for their helpful comments and suggestions.

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Authors and Affiliations

  1. Faculty of Computer Engineering, Islamic Azad University, North Tehran Branch, Tehran, Iran

    A. Mir

  2. Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran

    Jalal A. Nasiri

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  1. A. Mir
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  2. Jalal A. Nasiri
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Correspondence to Jalal A. Nasiri.

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Mir, A., Nasiri, J.A. KNN-based least squares twin support vector machine for pattern classification. Appl Intell 48, 4551–4564 (2018). https://doi.org/10.1007/s10489-018-1225-z

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