Comparing the Performance of Probabilistic Weighting Classification Techniques for Water Quality Assessment
Keywords:
Water Quality, Classification, Probabilistic Weighting Ensemble, Machine Learning, Model PerformanceAbstract
This research investigation explores the comparative performance of probability weighting classification techniques in the assessment of water quality. The dataset, sourced from Kaggle, comprises 7,999 records detailing water quality, characterized by 21 dimensions of chemical component quantities and another binary-class quality indicator. Through the integration of ensemble methods and the utilization of pairwise comparison techniques, the study demonstrates enhancements in precision, recall, and F-measure, achieving a minimum increase of 6.68%, albeit with a maximum trade-off of 5.16% in accuracy, when compared to single classifiers. These findings not only contribute to advancing single classification techniques but also lay the groundwork for the development of more resilient and dependable models. The implications of this research extend to practical applications in environmental monitoring practices, influencing policy decisions, and guiding interventions aimed at safeguarding water quality. By establishing a foundation for robust modeling, the study underscores its significance in shaping proactive measures for sustaining and preserving the quality of water resources.
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