Asset Details
Do you wish to reserve the book?
A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets
Do you wish to request the book?
A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets
2025
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
