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DOI: 10.14569/IJACSA.2020.0111137
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A New Hybrid KNN Classification Approach based on Particle Swarm Optimization

Author 1: Reem Kadry
Author 2: Osama Ismael

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.

  • Abstract and Keywords
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Abstract: K-Nearest Neighbour algorithm is widely used as a classification technique due to its simplicity to be applied on different types of data. The presence of multidimensional and outliers data have a great effect on the accuracy of the K-Nearest Neighbour algorithm. In this paper, a new hybrid approach called Particle Optimized Scored K-Nearest Neighbour was proposed in order to improve the performance of K-Nearest Neighbour. The new approach is implemented in two phases; the first phase help to solve the multidimensional data by making feature selection using Particle Swarm Optimization algorithm, the second phase help to solve the presence of outliers by taking the result of the first phase and apply on it a new proposed scored K-Nearest Neighbour technique. This approach was applied on Soybean dataset, using 10 fold cross validation. The experiment results shows that the proposed approach achieves better results than the K-Nearest Neighbour algorithm and it’s modified.

Keywords: K-nearest neighbour; outlier; multidimensional; particle swarm optimization; scored k-nearest neighbour

Reem Kadry and Osama Ismael, “A New Hybrid KNN Classification Approach based on Particle Swarm Optimization” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111137

@article{Kadry2020,
title = {A New Hybrid KNN Classification Approach based on Particle Swarm Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111137},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111137},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Reem Kadry and Osama Ismael}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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