Comparing Belief KNN, Adaptive KNN, Fuzzy KNN and System classification hybrid Algorithms: A comprehensive guide to understanding K-Nearest Neighbors

Ghada Rahal¹ , Islem Dridi²

1. Department of management from Ecole Supérieur des Sciences Economiques et Commerciales de Tunis, Tunisia.
2. Department of management from Ecole Supérieur des Sciences Economiques et Commerciales de Tunis, Tunisia.

Section:Review Paper, Product Type: Journal-Paper
Vol.11 , Issue.1 , pp.47-50, Feb-2023

Online published on Feb 28, 2023

Copyright © Ghada Rahal, Islem Dridi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This research is about to present and compare four of K-Nearest Neighbors (KNN) versions (Belief KNN, Adaptive KNN, Fuzzy KNN, System classification hybrid), that have recently been studied. Furthermore, indicate their limits. Computers may now ingest data without even being entirely programmed thanks to machine learning. Unsupervised learning and supervised are both types for machine learning. Computers adopt an objective in supervised learning which translates an input to an output based on training input-output pairs. K-Nearest Neighbors is among the most successful and frequently used in supervised learning methods. This study is the first step in trying to clarify the difference in algorithm between KNN versions. Results will show benefits and drawbacks of these four KNN varieties, otherwise, representing the critics for each KNN type. This work can be beneficial for researchers learning to improve their studies in supervised machines, by showing how KNN versions whether explain the research and subsequent comparison measures for distance regulation.

Key-Words / Index Term :
Machine learning, Supervised learning, KNN, Fuzzy KNN, System classification hybrid, Belief KNN, Adaptive KNN

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