Turkish Journal of Electrical Engineering and Computer Sciences
An improved version of multi-view k-nearest neighbors (MVKNN) for multipleview learning
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.
Machine learning, multi-view learning, classification, k-nearest neighbors
KIYAK, ELİFE ÖZTÜRK; BİRANT, DERYA; and BİRANT, KÖKTEN ULAŞ
"An improved version of multi-view k-nearest neighbors (MVKNN) for multipleview learning,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29:
3, Article 6.
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss3/6
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