Classification of chromosomes using nearest neighbor classifier

Kanita Karadjuzovic-Hadziabdic


This paper addresses automated classification of human chromosomes using k nearest neighbor classifier. k nearest neighbor classifier classifies objects according to the closest training sample in the feature space.  Various distance functions can be used in computation of how close the object is to the training sample. In this work various different distance functions are used to compare the performance of each. It was found that Euclidean distance function produces the best results.

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Copyright (c) 2015 SouthEast Europe Journal of Soft Computing

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

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This work is licensed under a Creative Commons Attribution 4.0 International License