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

Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan and William Wei Song

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 3, 1550147720911892

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.

Keywords: K-nearest neighbor; multi-label classification; random walk; similarity measurement (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147720911892 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:3:p:1550147720911892

DOI: 10.1177/1550147720911892

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().