A Weighted Similarity Measure for k-Nearest Neighbors Algorithm

Year 2019, , 393 - 400, 30.12.2019

Bergen Karabulut , Güvenç Arslan , Halil Murat Ünver

https://doi.org/10.18466/cbayarfbe.618964

Cited By: 2

Abstract

One of the most important problems in machine
learning, which has gained importance in recent years, is classification. The
k-nearest neighbors (kNN) algorithm is widely used in classification problem
because it is a simple and effective method. However, there are several factors
affecting the performance of kNN algorithm. One of them is determining an
appropriate proximity (distance or similarity) measure. Although the Euclidean
distance is often used as a proximity measure in the application of the kNN,
studies show that the use of different proximity measures can improve the performance
of the kNN. In this study, we propose the Weighted Similarity k-Nearest
Neighbors algorithm (WS-kNN) which use a weighted
similarity as proximity measure in the kNN algorithm. Firstly, it
calculates the weight of each attribute and similarity between the instances in
the dataset. And then, it weights similarities by attribute weights and creates
a weighted similarity matrix to use as proximity measure. The proposed
algorithm is compared with the classical kNN method based on the Euclidean
distance. To verify the performance of our algorithm, experiments are made on
10 different real-life datasets from the UCI (UC Irvine Machine Learning
Repository) by classification accuracy. Experimental results show that the
proposed WS-kNN algorithm can achieve comparative classification accuracy. For
some datasets, this new algorithm gives highly good results. In addition, we demonstrated
that the use of different proximity measures can affect the classification
accuracy of kNN algorithm.

Keywords

Classification, Feature Weighting, k-Nearest Neighbors, Weighted Similarity

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