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A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Author

Listed:
  • Zhen-Wu Wang
  • Si-Kai Wang
  • Ben-Ting Wan
  • William Wei Song

Abstract

The multi-label classification problem occurs in many real-world tasks where an object is naturally associated with multiple labels, that is, concepts. The integration of the random walk approach in the multi-label classification methods attracts many researchers’ sight. One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. In this article, we propose a novel multi-label classification algorithm based on the random walk graph and the K -nearest neighbor algorithm (named MLRWKNN). This method constructs the vertices set of a random walk graph for the K -nearest neighbor training samples of certain test data and the edge set of correlations among labels of the training samples, thus considerably reducing the overhead of time and space. The proposed method improves the similarity measurement by differentiating and integrating the discrete and continuous features, which reflect the relationships between instances more accurately. A label predicted method is devised to reduce the subjectivity of the traditional threshold method. The experimental results with four metrics demonstrate that the proposed method outperforms the seven state-of-the-art multi-label classification algorithms in contrast and makes a significant improvement for multi-label classification.

Suggested Citation

  • Zhen-Wu Wang & Si-Kai Wang & Ben-Ting Wan & William Wei Song, 2020. "A novel multi-label classification algorithm based on K-nearest neighbor and random walk," International Journal of Distributed Sensor Networks, , vol. 16(3), pages 15501477209, March.
  • Handle: RePEc:sae:intdis:v:16:y:2020:i:3:p:1550147720911892
    DOI: 10.1177/1550147720911892
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    Cited by:

    1. Andy Yiu-Chau Tam & Li-Wen Zha & Bryan Pak-Hei So & Derek Ka-Hei Lai & Ye-Jiao Mao & Hyo-Jung Lim & Duo Wai-Chi Wong & James Chung-Wai Cheung, 2022. "Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model," IJERPH, MDPI, vol. 19(20), pages 1-12, October.
    2. José M. Cuevas-Muñoz & Nicolás E. García-Pedrajas, 2023. "ML-k’sNN: Label Dependent k Values for Multi-Label k -Nearest Neighbor Rule," Mathematics, MDPI, vol. 11(2), pages 1-24, January.

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