Compactness-Weighted KNN Classification Algorithm

The K-Nearest Neighbor (KNN) algorithm is a widely used classical classification tool, yet enhancing the classification accuracy 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. Comparative experimental results on five real-world datasets demonstrate that the CKNN algorithm outperforms eight existing 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 datasets.