An improved version of multi-view k-nearest neighbors (MVKNN) for multiple view learning

Öztürk Kıyak E., Birant D., Birant K. U.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.29, no.3, pp.1401-1428, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 3
  • Publication Date: 2021
  • Doi Number: 10.3906/elk-2005-59
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1401-1428
  • Keywords: Machine learning, multi-view learning, classification, k-nearest neighbors, CLASSIFICATION
  • Dokuz Eylül University Affiliated: Yes


Multi-view learning (MVL) is a special type of machine learning that utilizes more than one views, where views include various descriptions of a given sample. Traditionally, classification algorithms such as k-nearest neighbors (KNN) are designed for learning from single-view data. However, many real-world applications involve datasets with multiple views and each view may contain different and partly independent information, which makes the traditional single-view classification approaches ineffective. Therefore, this article proposes an improved MVL algorithm, called multi-view k-nearest neighbors (MVKNN), based on the existing KNN algorithm. The experimental results conducted in this research show that a significant improvement is achieved by the proposed MVKNN algorithm compared to the well-known machine learning algorithms (KNN, support vector machine, decision tree, and naive bayes) in the case of multi-view data. The results also show that our method outperforms the state-of-the-art multi-view learning methods in terms of accuracy.