Authors : Zara Wong

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/4uurzrsv

Scribd : https://tinyurl.com/mrs9hewe

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY517

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The k-Nearest Neighbour (k-NN) algorithm is a simple and intuitive classification algorithm used for pattern recognition and classification tasks. This research paper aims to address a gap in literature by exploring the relationship between sample size and the performance of the k-Nearest Neighbour (k-NN) algorithm. Through intensive experimental analysis of secondary data, we investigate how varying sample sizes influence the algorithm’s classification accuracy, computational efficiency, and generalization capabilities. Our findings reveal that an ideal scope for sample sizes is >190, with minimal differing results beyond that point. The maximum of the graph is 340, suggesting it to be the optimal value for ideal accuracy for this training model and scope. These results contribute to a deeper understanding of the proper application of the k-NN. These findings contribute to a deeper understanding of the complex interplay between sample sizes and k NN algorithm performance, aiding practitioners in making informed decisions when employing this method in realworld applications, and suggest the ideal value for sample size.

Keywords : Systems Software; Algorithms; Machine Learning; k-Nearest Neighbour; Sample Size.

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