Abstract: The K-Nearest Neighbor (KNN) algorithm is a widely used classical classification tool, yet enhancing the classification ac-curacy for multi-feature large datasets remains a challenge. The paper introduces a Compactness-Weighted KNN classification algorithm using a weighted Minkowski distance (CKNN) to address this. Due to the variability in sample distribution, a method for deriving feature weights based on compactness is designed. Subsequently, a formula for calculating the weighted Minkowski distance using compactness weights is proposed, forming the basis for developing the CKNN algorithm. Com-parative experimental results on five real-world datasets demonstrate that the CKNN algorithm outperforms eight exist-ing variant KNN algorithms in Accuracy, Precision, Recall, and F1 performance metrics. The test results and sensitivity analysis confirm the CKNN's efficacy in classifying multi-feature da-tasets.

Bengting Wan, Zhixiang Sheng, Wenqiang Zhu and Zhiyi Hu, “Compactness-Weighted KNN Classification Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150922

@article{Wan2024,
title = {Compactness-Weighted KNN Classification Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150922},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150922},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Bengting Wan and Zhixiang Sheng and Wenqiang Zhu and Zhiyi Hu}
}