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DOI: 10.14569/IJACSA.2022.0130476
PDF

An Optimized Hybrid Fuzzy Weighted k-Nearest Neighbor with the Presence of Data Imbalance

Author 1: Soha A. Bahanshal
Author 2: Rebhi S. Baraka
Author 3: Bayong Kim
Author 4: Vaibhav Verdhan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 4, 2022.

  • Abstract and Keywords
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Abstract: We present an optimized hybrid fuzzy Weighted k-Nearest Neighbor classification model in the presence of imbalanced data. More attention is placed on data points in the boundary area between two classes. Finding greater results in the general classification of imbalanced data for both the minority and the majority classes. The fuzzy weighted approach assigns large weights to small classes and small weights to large classes. It improves the classification performance for the minority class. Experimental results show a higher average performance than other relevant algorithms, e.g., the variants of kNN with SMOTE such as Weighted kNN alone and Fuzzy kNN alone. The results also signify that the proposed approach makes the overall solution more robust. At the same time, the overall classification performance on the complete dataset is also increased, thereby improving the overall solution.

Keywords: Imbalanced data; fuzzy weighted kNN; SMOTE; classification model; optimized hybrid kNN

Soha A. Bahanshal, Rebhi S. Baraka, Bayong Kim and Vaibhav Verdhan, “An Optimized Hybrid Fuzzy Weighted k-Nearest Neighbor with the Presence of Data Imbalance” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130476

@article{Bahanshal2022,
title = {An Optimized Hybrid Fuzzy Weighted k-Nearest Neighbor with the Presence of Data Imbalance},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130476},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130476},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Soha A. Bahanshal and Rebhi S. Baraka and Bayong Kim and Vaibhav Verdhan}
}



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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