000			01822nam a22001817a 4500
003			FT8929
005			20260112151233.0
050			_aQA76.9 A43 B56 2025
100	1		_aBinalla, Merlinda C.; Villanueva, Maisie Allena F.
245			_aAn enhancement of the gaussian naïve bayes algorithm applied to air quality classification
264		1	_cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
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337			_2 unmediated _a unmediated _b unmediated
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505			_aABSTRACT: 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.
942			_2lcc _cMS
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