An enhancement of the gaussian naïve bayes algorithm applied to air quality classification

ABSTRACT: The Gaussian Naïve Bayes Algorithm is a matching learning technique based upon the Bayes Theorem. It is commonly used for classification tasks to calculate the likelihood of events. This study developed an enhanced GNB algorithm to classify the air quality in Pamantasan ng Lungsod ng Maynila. The enhancement made in this study sought to increase the classification performance of the traditional GNB against feature independence using the Boruta algorithm, zero frequency issues with the Parzen-Rosenblatt Window method, and inconsistencies across diverse datasets through SMOTE-ENN, integrating these methods into a three-layered technique to characterize air quality. OpenWeather-AQI and USA-AQI datasets were used to evaluate the algorithm. The algorithm’s accuracy improved from 71.77% to 75.60% (3.83%) in the Open Weather-AQI dataset. In comparison, the other dataset showed a 9.57% improvement, increasing from 59.33% to 68.90%. These results showcase how the enhanced GNB algorithm outperforms the traditional one. Thus, the Enhanced GNB Algorithm effectively improves classification accuracy and demonstrates its potential as a reliable methods for assessing air quality.