Improving K-Nearest Neighbor Algorithm Performance Using Modified Distance Measures
Keywords:
Machine learning, k-nearest neighbor, distance measure, accuracy measures.Abstract
Classification in the field of machine learning refers to the process of identifying and categorizing objects within a given dataset. Distance-based algorithms are widely used for data classification problems. The k-nearest neighbor (KNN) classification is based on measuring the distances between the test sample and the training samples to determine the final classification output.KNN relies on measuring similarity to group data into classes based on how similar their features are without relying on probabilities but rather utilizing distance metrics, for classification purposes.
References
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Alfeilat, H.A.A. Hassanat, A B.A., Lasassmeh, O., Tarawneh, A.S., Alhasanat, M.B.A., Salman, H.S.E. & Prasath, V.B.S. (2019). Effects of Distance Measure Choice on K Nearest Neighbor Classifier Performance: A Review. Big Data. 7(4) 221-248.
