A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets

Class imbalance poses a significant challenge in water quality classification, often leading to biased predictions and diminished accuracy for minority classes. This study introduces SMOTE-PCA-HDBSCAN, a novel oversampling framework that integrates the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples, Principal Component Analysis (PCA) to enhance data separability, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to remove synthetic data noise. The cleaned synthetic data is then merged with the original dataset to form a balanced, noise-reduced training set. Comparative evaluations against SMOTE, SMOTE-DBSCAN, SMOTE-PCA-DBSCAN, SMOTE-ENN, and SMOTE-Tomek Links reveal that SMOTE-PCA-HDBSCAN consistently improves sensitivity for minority classes (Clean: 4.76% to 28.57%; Polluted: 38.09% to 61.90%) while maintaining high accuracy for the majority class. These results demonstrate the robustness of SMOTE-PCA-HDBSCAN in addressing class imbalance, offering a valuable tool for enhancing predictive models in environmental monitoring and other domains with imbalanced datasets.