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Multi-category laplacian least squares twin support vector machine

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Abstract

In this paper, we have formulated a Laplacian Least Squares Twin Support Vector Machine called Lap-LST-KSVC for semi-supervised multi-category k-class classification problem. Similar to Least Squares Twin Support Vector Machine for multi-classification(LST-KSVC), Lap-LST-KSVC, evaluates all the training samples into “1-versus-1-versus-rest” classification paradigm, so as to generate ternary output {−1, 0, +1}. Experimental results prove the efficacy of the proposed method over other inline Laplacian Twin Support Vector Machine(Lap-TWSVM) in terms of classification accuracy and computational time.

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Acknowledgment

We are thankful to Prof. Suresh Chandra for his encouragement during the preparation of our manuscript. The authors would also like to thank the editor and the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. South Asian University, New Delhi, India

    Reshma Khemchandani & Aman Pal

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  1. Reshma Khemchandani
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  2. Aman Pal
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Correspondence to Reshma Khemchandani.

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Khemchandani, R., Pal, A. Multi-category laplacian least squares twin support vector machine. Appl Intell 45, 458–474 (2016). https://doi.org/10.1007/s10489-016-0770-6

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