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Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification

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Abstract

Semi-supervised learning has attracted researchers due to its advantages over supervised learning. In this paper, an extremely fast multi-category classification algorithm, termed as weighted ternary decision structure (WTDS) is proposed. WTDS is a generic algorithm that can extend any binary classifier into multi-category framework. This work also proposes a novel semi-supervised binary classifier termed as Weighted Laplacian least-squares twin support vector machine which is further extended using WTDS. The novel semi-supervised classifier obtains the solution by formulating a pair of Unconstrained Minimization Problems which are solved as systems of linear equation. WTDS takes advantage of the strengths of the classifier and efficiently constructs the multi-category classifier model in the form of a decision structure. WTDS outperforms other state-of-the-art multi-category approaches in terms of classification accuracy and time complexity. To confirm the feasibility and efficacy of proposed algorithm, experiments are conducted on benchmark UCI datasets.

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Notes

  1. The bold figures indicate best value for the given dataset.

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Authors and Affiliations

  1. Vivekananda Institute of Professional Studies, New Delhi, India

    Pooja Saigal

  2. Department of Computer Science, South Asian University, New Delhi, India

    Reshma Rastogi

  3. Ex Faculty, Department of Mathematics, Indian Institute of Technology, New Delhi, India

    Suresh Chandra

Authors
  1. Pooja Saigal
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  2. Reshma Rastogi
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  3. Suresh Chandra
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Correspondence to Pooja Saigal.

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Saigal, P., Rastogi, R. & Chandra, S. Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification. Neural Process Lett 52, 1555–1582 (2020). https://doi.org/10.1007/s11063-020-10323-7

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